META optimization

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A new nature inspired algorithm, that simulates the mating behavior of the bumble bees, the Bumble Bees Mating Optimization (BBMO) algorithm, is presented in this paper for solving global unconstrained optimization problems. The performance of the algorithm is compared with other popular metaheuristic and nature inspired methods when applied to the most classic global unconstrained optimization problems. The methods used for comparisons are Genetic Algorithms, Island Genetic Algorithms, Differential Evolution, Particle Swarm Optimization, and the Honey Bees Mating Optimization algorithm. A high performance of the proposed algorithm is achieved based on the results obtained.

Metaheuristic algorithms have emerged as indispensable tools for solving complex structural design problems characterized by nonlinearity, high dimensionality, and conflicting objectives. Traditional optimisation techniques often fall short in navigating such multifaceted design landscapes, necessitating more adaptive and robust approaches. This paper presents a comparative analysis of five metaheuristic algorithms applied to structural engineering problems: Genetic Algorithms (GA), Osprey Optimisation Algorithm (OOA), Quantum Annealing-based Structural Optimisation (QASO), Ensemble Laplacian Biogeography-Based Sine Cosine Algorithm (ELBBSC), and the Fitness Distance Balance Modified Metaheuristic (FDB-Meta). Recent developments since 2020 are emphasized to highlight innovations in convergence dynamics, robustness, and computational efficiency. Benchmark structural problems, such as steel moment frames, cable-stayed bridges, and RC slab bridges, are used to evaluate algorithm performance across various criteria, including convergence speed, solution quality, robustness, scalability, and parameter sensitivity. The results indicate that while GA remains a foundational method, newer algorithms, such as FDB-Meta and OOA, demonstrate significant improvements in both cost efficiency and reliability. This study contributes a systematic guideline for algorithm selection in structural design optimisation and outlines avenues for future research in hybrid metaheuristic development and adaptive parameter tuning.

Electric smelting furnaces, applied in the smelting process of infusible mineral, are highly energy-intensive. In China, they waste a huge amount of electric energy, but yield a small quantity of valuable metals due to the lack of optimized load scheduling strategies. In this paper, we design a multiobjective load scheduling method to minimize the electricity cost and maximize the production output and product quality. Firstly a load scheduling model is developed based on a least square support vector machine, which is a robust empirical model with simple formulation and low sensitivity to external disturbance. We utilize a modified multiobjective particle swarm optimization algorithm to solve the optimization model. The proposed algorithm adopts a supervised population initialization that reuses the past optimal solutions and digs out new candidate solutions to guide the current optimization. An elaborate constraint-handing strategy is devised, which repairs the infeasible solutions that violate the maximum demand constraint and reserve the ones that violate one production constraint but performing excellently on the other production targets. The case study on a typical magnesia-smelting plant shows that the proposed multi-objective load scheduling model and algorithm can achieve an increase of about 14.5% in the production output, an improvement of about 0.46% of the magnesia in the product, and a slight saving in electricity cost.

It is challenging to solve constrained multi-objective optimization problems (CMOPs). Different from the traditional multi-objective optimization problem, the feasibility, convergence, and diversity of the population must be considered in the optimization process of a CMOP. How these factors are balanced will affect the performance of the constrained multi-objective optimization algorithm. To solve this problem, we propose a dual-population multi-objective optimization evolutionary algorithm. The proposed algorithm can make good use of its secondary population and alternative between evolution and degeneration according to the state of the secondary population to provide better information for the main population. The test results of three benchmark constrained multi-objective optimization problem suites, and four real-world constrained multi-objective optimization problems show that the algorithm is better than existing dual-population multi-objective optimization, especially when there is a distance between the unconstrained PF and the constrained PF.

Most of the real-life applications have many constraints and they are considered as constrained optimization problems (COPs). In this paper, we present a new hybrid genetic differential evolution algorithm to solve constrained optimization problems. The proposed algorithm is called hybrid genetic differential evolution algorithm for solving constrained optimization problems (HGDESCOP). The main purpose of the proposed algorithm is to improve the global search ability of the DE algorithm by combining the genetic linear crossover with a DE algorithm to explore more solutions in the search space and to avoid trapping in local minima. In order to verify the general performance of the HGDESCOP algorithm, it has been compared with 4 evolutionary based algorithms on 13 benchmark functions. The experimental results show that the HGDESCOP algorithm is a promising algorithm and it outperforms other algorithms.

Ant Colony Optimization Algorithm is a meta-heuristic, multi-agent technique that can be applied for solving difficult NP-Hard Combinatorial Optimization Problems like Traveling Salesman Problem (TSP), Job Shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP) and many more. The Positive Feedback Mechanism and Distributed Computing ability makes it very robust in nature. The artificial ants implement a randomized construction heuristic which makes probabilistic decisions as a function of artificial pheromone trails to solve the problems that are dependent on the input data. In spite of ACO having global searching ability and high convergence speed towards optimal solutions, it has some limitations like low population scattering ability and no systematic way of startup. To overcome these problems, various hybrids of ACO with other algorithms like Dynamic Programming, Genetic Algorithm and Particle Swarm Optimization have been proposed to provide better results than using ACO in...

Robot mobile otonom (autonomous mobile robot, AMR) banyak digunakan baik di industri maupun lingkungan rumah tangga. Robot jenis ini mempunyai kemampuan bergerak menuju tempat tertentu secara otomatis. Penggunaan GPS pada tempat tertutup dan area terbatas belum dapat memenuhi kebutuhan karena keterbatasan resolusi GPS. Pada penelitian ini dikembangkan robot mobile yang mampu menuju titik tertentu dengan memanfaatkan sensor rotary encoder dan metode odometry. Koordinat tujuan diberikan ke robot dan kemudian secara otomatis robot bergerak ke arah titik koordinat tujuan. Pengujian dilakukan dengan memberikan tujuan dengan jarak berbeda yaitu 60 cm, 100 cm, 150 cm, 200 cm, dan 250 cm. Rata-rata galat perhitungan odometry yang dihitung pada jarak tersebut adalah sebesar 0.5 cm, 2.63 cm, 1.65 cm, dan 1.1 cm. Pada pengukuran secara riil didapatkan galat sebesar 7cm, 13cm, 10cm, dan 20 cm. Pengujian rute langsung mempunyai nilai galat yang paling kecil dibanding pengujian rute 1 dan rute 2 ...

In this paper the synthesis of linear array geometry with minimum sidelobe level using a new class of Particle Swarm Optimization technique namely Improved Particle Swarm Optimization (IPSO) is described. The IPSO algorithm is a newly proposed, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and

Modeling and solving complex engineering problems requires new algorithmic tools. A promising new tool for numerical optimization is the genetic algorithm. This paper presents two interesting applications using the genetic algorithm. The first optimizes a function that has a subjective output: music. The second uses continuous parameters rather than the traditional binary parameters to find solutions to a nonlinear partial differential equation. A brief introduction to the continuous parameter genetic algorithm is given.

Optimizing water distribution systems is an essential part of water resources allocation planning. It leads to challenging combinatorial optimization problems, for which meta-heuristics have been applied, notably genetic algorithms and ant colony optimization. The present paper introduces the application of the physarum algorithm, a recent biologically inspired algorithm, utilized hitherto for path finding problems in networks. Direct comparison is presented to ant colony optimization applied to a typical water supply system.

Production planning is the important part of controlling the cost spent by the company. In this research, production planning model is linear integer programming model with constraints : production, worker, and inventory. Linear integer programming as optimization problem can be solved by exact method like branch and bound, cutting plane or heuristic method. In this paper, we use heurisitic like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving production planning optimization in approaching. GA uses natural selection process of chromosomes while PSO is inspired by the behavior of flocks of birds, swarm of insects, or school of fish. The simulations show that both GA and PSO can find optimal solution of fertilizer production planning in approaching.

The evolutionary computation approach provides a method for solving complex optimization problems by simulating natural evolutionary processes such as selection, mutation, and crossover. This study evaluates the performance of several evolutionary computation algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), in the context of task scheduling and planning. Each algorithm is tested in a real-time production system scenario, with evaluation indicators including result accuracy, processing efficiency, and convergence capability. Based on the experimental results, GA produces consistent solutions, PSO achieves faster results, while DE demonstrates superior performance in handling large-scale problems. The findings of this study serve as a valuable reference in selecting the appropriate evolutionary algorithm to meet the specific needs of scheduling systems.

Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel optimization algorithm, called Single Candidate Optimizer (SCO), that relies only on a single candidate solution throughout the whole optimization process. The proposed algorithm implements a unique set of equations to effectively update the position of the candidate solution. To balance exploration and exploitation, SCO is integrated with the two-phase strategy where the candidate solution updates its position differently in each phase. The effectiveness of the proposed approach is validated by testing it on thirty three classical benchmarking functions and four real-world engineering problems. SCO is compared with three well-known optimization algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, and Gravitational Search Algorithm and with four recent high-performance algorithms: Equilibrium Optimizer, Archimedes Optimization Algorithm, Mayfly Algorithm, and Salp Swarm Algorithm. According to Friedman and Wilcoxon rank-sum tests, SCO can significantly outperform all other algorithms for the majority of the investigated problems. The results achieved by SCO motivates the design and development of new single-solution-based optimization algorithms to further improve the performance. The source code of SCO is publicly available at: . mathw orks. com/ matla bcent ral/ filee xchan ge/ 116100-single-candi date-optim izer.

Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-parameter optimization problems, demonstrates its outstanding local search capability. In this study, two mechanisms are proposed and integrated into particle swarm optimization for single-objective numerical optimization. A novel ratio adaptation scheme is utilized for calculating the proportion of subpopulations and intermittently invoking the sequential quadratic programming for local search start from the best particle to seek a better solution. The novel particle swarm optimization variant was validated on CEC2013, CEC2014, and CEC2017 benchmark functions. The experimental results demonstrate impressive performance compared with the state-of-the-art particle swarm optimization-based algorithms. Furthermore, the results also illustrate the effectiveness of the two mechanisms when cooperating to achieve significant improvement.

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design's central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke's law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algorithm is tested and compared with the standard PSO and DE methods on 14 benchmark problems to illustrate that DPSE has the potential to achieve a faster convergence and a better solution. Simulation results show that, on the average, DPSE outperforms DE by 39.20% and PSO by 14.92% on the 14 benchmark problems. To show the feasibility of the proposed strategy on a real-world optimization problem, an application of DPSE to optimize the parameters of active disturbance rejection control (ADRC) in PUMA-560 robot is presented.

In this paper, a hybrid technique for global based on the genetic algorithm and a determinktic method is presented. A potential advantage of the hybrid method compared to the genetic algorithm is that global o p t h b t i ( ~1 can be performed more efficiently. An intrinsic pmb1em of the hybrid tmKques is rehted to the moment of stopping the stochastic routin; to launch the one. This is investigated using some natural criteria for the commutation between the two methods. The results show that it is possible to gain in efficiency and in accuracy but the criterion is usually problem dependent. Finally, to show the solution of a real problem, the hybrid algoritlann is coupled to a 2D cade based on the boundary element a conneetor of 145 kV GIs.

Optimization problem is one of the most difficult and challenging problems that has received considerable attention over the last decade. Researchers have been constantly investigating better ways to solve it. Recently, one optimization technique called firefly algorithm has gained the interest of many researchers. This algorithm is a type of swarm intelligence algorithm based on the reaction of a firefly to the light of other fireflies. In this paper, we propose an improvement on the original firefly algorithm. The proposed algorithm takes into account not only the firefly's reaction to light but also the following contributing factors: firefly's gene exchange, its pheromone, and the impact the wind has on pheromone dispersion. We tested our proposed algorithm against the traditional firefly algorithm and the original genetic algorithm with six standard benchmark functions and found that our algorithm is not only more effective but also faster than the other two algorithms.

Nature-inspired algorithms, such as Particle swarm optimization (PSO), Ant colony optimization (ACO), and Firefly algorithm, are well known for solving NP-hard optimization problems. They are capable of obtaining optimal solutions in a reasonable time. The algorithm presented in this paper is a combination of a firefly mating concept and genetic algorithm. Genetic algorithm is used as the core of the algorithm while a firefly mating concept is used to compose a new selection operator. The proposed algorithm is tested on four standard benchmark functions. Experimental results have confirmed that the proposed algorithm is not only computationally more efficient than both the original firefly algorithm and the genetic algorithm but also almost always ensure the optimal solutions.

Optimization of training neural network using particle swarm optimization (PSO) and genetic algorithm (GA) is a solution backpropagation’s problem. PSO often trapped in premature convergent (convergent at local optimum) and GA takes a long time to achieve convergent and crossover makes worse the results. In this research adaptive mutation particle swarm optimization and backpropagation (AMPSO-BP) is used for training the neural network of the iris plant, breast cancer, wine, glass identification and pima indian diabetes. The addition of PSO with adaptive mutation to prevent premature convergent and BP to increase the efficiency of local searching. AMPSO-BP training results will be compared with the GA and BP. The test results showed AMPSO-BP is able to optimize the process of training the neural network. AMPSO-BP more rapidly achieve the minimum error (global minimum), fast convergent and have the ability memorization and generalization with more accurate results than the other meth...

The most and major popular technique in evolutionary computation research has been the genetic algorithm. Mostly in the Genetic Algorithm(GA), the representation used is a fixed-length bit string. Each position in the string represents a particular feature of an individual and the particular value stored in that corresponding position. Particle Swarm Optimization(PSO) is used to find the optimal fitness value.Simulations are performed over the various standard test data and comparisons are performed with Genetic Algorithm(GA). The experimental results show that proposed PSO based method performs better than the GA method.

In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within particle swarm optimization to improve the algorithm’s search capabilities in dynamically changing environments. For algorithm testing, the Generalized Moving Peaks Benchmark was used. The experiments were performed for four benchmark settings, and the sensitivity analysis to the main parameters of algorithms is performed. It is shown that applying the mutation operator from differential evolution to the personal best positions of the particles allows for improving the algorithm performance.

Particle swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single-and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in particle swarm optimization to improve its performance over the years. Most of these approaches are observed to increase the number of user-defined parameters and algorithmic steps resulting in an increased complexity of the algorithm. This paper presents a simplified multi-objective particle swarm optimization algorithm in which the exploitation (guided) and exploration (random) moves are simplified using a detailed qualitative analysis of similar existing operators present in the real-coded elitist non-dominated sorting genetic algorithm and the particle swarm optimization algorithm. The developed algorithm is then tested quantitatively on 30 well-known benchmark problems and compared with a real-coded elitist non-dominated sorting genetic algorithm, and its variant with a simulated binary jumping gene operator and multi-objective non-dominated sorting particle swarm optimization algorithm. In the comparison, the developed algorithm is found to be superior in terms of convergence speed. It is also found to be better with respect to four recent multi-objective particle swarm optimization algorithms and four differential evolution variants in an extended comparative analysis. Finally, it is applied to a newly formulated industrial multi-objective optimization problem of a residue (bottom product from the crude distillation unit) fluid catalytic cracking unit where it shows a better performance than the other compared algorithms.

The demand for efficient solutions to optimization problems with uncertain and stochastic data is increasing. Probabilistic traveling salesman problem (PTSP) is a class of Stochastic Combinatorial Optimization Problems (SCOPs) involving partially unknown information about problem data with a known probability distribution. It consists to minimize the expected length of the tour where each customer requires a visit only with a given probability, at which customers who do not need a tour are just ignored without further optimization. Since the PTSP is NP-hard, the usage of metaheuristic methods is necessary to solve the problem. In this paper, we present the Ant Colony Optimization (ACO) algorithm combined with the Levy Flight mechanism (LFACO), which is based on Levy distribution to balance searching space and speed global optimization. Experimental results on a large number of instances show that the proposed Levy ACO algorithm on the probabilistic traveling salesman problem allows to obtain better results compared with the classical ACO algorithm.

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by .

This paper introduces the Greedy Man Optimization Algorithm (GMOA), a novel bio-inspired metaheuristic approach for solving complex optimization problems. Inspired by competitive individuals resisting change, GMOA incorporates two unique mechanisms: MMO resistance, which prevents premature replacement of solutions, and periodic parasite removal, which promotes diversity and avoids stagnation. The algorithm is evaluated on standard benchmark functions, including Sphere, Rastrigin, Rosenbrock, and Griewank, and its performance is compared with established algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO). Results demonstrate that GMOA outperforms these methods in terms of solution quality, convergence rate, and robustness. Statistical significance tests validate the reliability of the results. GMOA’s ability to balance exploration and exploitation makes it a promising tool for various real-world applications, including supply chain optimization and healthcare resource allocation.

This paper demonstrates several optimization techniques which comprise Genetic algorithm (GA), Ant Colony optimization (ACO) and Particle swarm optimization (PSO). The proposed paper enforces the concept of artificial intelligence to detect minima / maxima by applying set of mathematical benchmark functions. For Optimization technique, artificial intelligence used which comprise of several algorithms like Particle swarm optimization, genetic algorithm, ant colony optimization, neural network and fuzzy system. The proposed work will use particle swarm intelligence and genetic intelligence. This paper bestows comparison between PSO and GA according to performance. In random search algorithm, GA cannot detect the global optimization solution. The benchmark functions used under these algorithms are Rosenbrock, Griewank, Ackley and Sphere. They have multiple local minima / maxima and single global minima / maxima. Neural network has propensity to strikes at local minima / maxima. Result ...

In the field of engineering, complex problems often arise that require solutions. The implementation of specific algorithms to tackle these problems becomes an essential part of this area, and optimizing these algorithms plays a crucial role in achieving good results with the available resources. The Vehicle Routing Problem (VRP) has been a central topic in distribution and logistics for decades. New VRP models and tools are continuously developed to address the challenges of modern logistics. The Energy Minimizing Vehicle Routing Problem (EMVRP) is a VRP model where the objective is to minimize the total amount of energy consumed by a fleet of vehicles. The VRP literature has focused on solving the problem using a variety of approaches and techniques, including exact methods, heuristics, metaheuristics, and hybrid algorithms. Hybrid algorithms combine different techniques, such as heuristics and metaheuristics, to obtain more effective solutions. This work presents the implementation of four hybrid algorithms to tackle the EMVRP. These algorithms combine machine learning clustering techniques, such as K-Means and K-Medoids, with metaheuristic approaches inspired by ant colony systems. The proposed algorithms are: Free Ant + K-Means, Free Ant + K-Medoids, Restricted Ant + K-Means, and Restricted Ant + K-Medoids. The Free Ant and Restricted Ant algorithms were subjected to testing using different instances from CVRPLIB, and the obtained results are promising compared to other metaheuristic techniques described in the literature. However, these results also indicate a wide scope for experimentation and algorithm tuning. INDEX TERMS Ant colony system, clustering techniques, complex problems, energy minimizing vehicle routing problem (EMVRP), free ant, heuristics, hybrid algorithms, k-means, k-medoids, machine learning, restricted ant, vehicle routing problem (VRP)

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

PurposeThe purpose of this study is to investigate and compare the ability of a new optimization technique based on the emulation of the immune system to detect the global maximum with multimodal functions and to test the capability of exploring the parameter space with respect to clustering enhanced Genetic Algorithms (GA).Design/methodology/approachBoth algorithms have been tested on analytical test functions and on numerical functions of applicative interest. A set of performance criteria has been defined in order to numerically compare the performances of both optimization strategies.FindingsResults show the great ability of Artificial Immune Systems (AIS) in thoroughly exploring the space of variables. On the other side, GA are faster to converge to the global optimum, but selection pressure can reduce the number of detected local optima.Originality/valueThis work is an attempt to assess the performances of a relatively new optimization algorithm based on AIS and to find its be...

Over the last two decades, many different genetic algorithms (GAs) have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms has shown consistent performance over a range of real world problems. The success of any GA depends on the design of its search operators, as well as their appropriate integration. In this paper, we propose a GA with a new multi-parent crossover. In addition, we propose a diversity operator to be used instead of mutation and also maintain an archive of good solutions. Although the purpose of the proposed algorithm is to cover a wider range of problems, it may not be the best algorithm for all types of problems. To judge the performance of the algorithm, we have solved aset of constrained optimization benchmark problems, as well as 14 well-known engineering optimization problems. The experimental analysis showed that the algorithm converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems.

A Metaheuristic Optimization is a group of algorithms that are widely studied and employed in the scientific literature. Typically, metaheuristics algorithms utilize stochastic operators that make each iteration unique, and they frequently contain controlling parameters that have an impact on the convergence process since their impacts are mostly neglected in most optimization literature, making it difficult to draw conclusions. This paper introduced the Big Bang-Big Crunch (BB-BC) metaheuristic algorithm to evaluate the performance of a metaheuristic algorithm in relation to its control parameter. It also demonstrates the effects of varying the values of BB-BC in solving. The "Welded Beam Design problem" is a well-known engineering optimization problem that is classified as a Single-Objective Constrained Optimization issue. Multiple starting parameter values for the BB-BC are evaluated as part of the experimental findings. This is done in an attempt to find the algorithm's optimal starting settings. The lowest, maximum, and mean values of the penalized objective functions are then computed. Finally, the BB-BC results are compared with various metaheuristics algorithms.

In this paper we compare two methods for forming reduced models to speed up geneticalgorithm-based optimization. The methods work by forming functional approximations ofthe fitness function which are used to speed up the GA optimization. One method speeds up the optimization by making the genetic operators more informed. The other method speeds up the optimization by genetically engineering some individuals instead of using the regular Darwinian evolution approach. Empirical results in several ...

Mathematical optimization refers to the process of finding the values of variables that maximize or minimize a function. Structural optimization is the process of designing a structure in such a way as to minimize its weight or cost, while meeting a set of performance requirements, ensuring that it is robust, lightweight, and efficient. Two large categories of optimization algorithms are mathematical and metaheuristic algorithms. The ones of the first rely on mathematical principles, are deterministic and exact but may fail if the problem is too large or complex. The latter category, metaheuristics, represents algorithms that are used to find approximate solutions. They are high-level strategies that guide the search toward a good solution, rather than being a specific, deterministic algorithm. They are often used for problems where it is difficult or impractical to find the optimal solution using exact methods. Metaheuristics typically involve iteratively improving a solution throu...

This paper introduces the problem of scheduling jobs on parallel plastic extrusion lines where each line is composed of one or more than one extruder. Although there are some similarities between the introduced problem and the non-identical parallel machines scheduling problems with sequence-dependent setup times, limited additional resources and machine eligibility restrictions, the problem considered in this paper is a generalization of the parallel machine scheduling problem. This is because in parallel machines scheduling each job requires only one machine but in our case some jobs require more than one machine. Thus, our problem reduces to the parallel machine scheduling problem if all jobs require only one machine. This paper describes the problem of scheduling parallel extrusion lines, its industrial context, and develops a mixed-linear formulation to model the problem. This formulation allowed solving instances of up to 15 jobs. In addition, we developed four metaheuristics: a simulated annealing algorithm, a tabu search heuristic, a genetic algorithm, and a greedy randomized adaptive search procedure. These metaheuristics can be used to solve real-life instances of the problem. A numerical experiment shows that the proposed metaheuristics produce excellent solutions. Some of the proposed simulated annealing adaptations and of the tabu search heuristics obtained solutions with less than 2% deviation from the optimum.

Weather forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of weather systems remains a challenge for traditional statistical models. Apart from Auto Regressive time forecasting models like ARIMA, deep learning techniques (Vanilla ANNs, LSTM and GRU networks), have shown promise in improving forecasting accuracy by capturing temporal dependencies. This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), to automate the search for optimal hyperparameters in these model architectures. Metaheuristic algorithms excel in global optimization, offering robustness, versatility, and scalability in handling non-linear problems. We present a comparative analysis of different model architectures integrated with metaheuristic optimization, evaluating their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The results demonstrate the potential of metaheuristic algorithms in enhancing weather forecasting accuracy & helps in determining the optimal set of hyper-parameters for each model. The paper underscores the importance of harnessing advanced optimization techniques to select the most suitable metaheuristic algorithm for the given weather forecasting task.

A deep energy retrofit of building envelopes is a vital strategy to reduce final energy use in existing buildings towards their net-zero emissions performance. Building energy modeling is a reliable technique that provides a pathway to analyze and optimize various energy-efficient building envelope measures. However, conventional optimization analyses are time-consuming and computationally expensive, especially for complex buildings and many optimization parameters. Therefore, this paper proposed a novel optimization algorithm, MEVO (metamodel-based evolutionary optimizer), developed to efficiently identify optimal retrofit solutions for building envelopes while minimizing the need for extensive simulations. The key innovation of MEVO lies in its integration of evolutionary techniques with design-of-computer experiments, machine learning, and metaheuristic optimization. This approach continuously refined a machine learning model through metaheuristic optimization, crossover, and mutation operations. Comparative assessments were conducted against four alternative metaheuristic algorithms and Bayesian optimization, demonstrating MEVO's effectiveness in reliably finding the best solution within a reduced computation time. A hypothesis test revealed that the proposed algorithm is significantly better than Bayesian optimization in finding the best cost values. Regarding computation time, the proposed algorithm is 4-7 times faster than the particle swarm optimization algorithm and has a similar computational speed as Bayesian Optimization.

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization). 

The swarm of particle optimization algorithm is among the most important tools in finding the optimal solution to nonlinear optimization problems. The main goal of this research is an expanded study by developing an effective algorithm to find the optimal solution based on the speed of convergence. The study also included comparing the results with an algorithm with the same orientation, as well. The results showed the superiority of the developed algorithm based on the results obtained.

In this paper, we illustrate a novel optimization approach based on Multi-objective Particle Swarm Optimization (MOPSO) and Fuzzy Ant Colony Optimization (FACO). The basic idea is to combine these two techniques using the best particle of the Fuzzy Ant algorithm and integrate it as the best local Particle Swarm Optimization (PSO), to formulate a new approach called hybrid MOPSO with FACO (H-MOPSO-FACO). This hybridization solves the multi-objective problem, which relies on two time performance criteria and the shortest path. Experimental results illustrate that the proposed method is efficient.

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA-jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon's rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

This paper describes the development of a new hybrid meta-heuristic of optimization based on a viral lifecycle, specifically the retroviruses (the nature's swiftest evolvers), called Retroviral Iterative Genetic Algorithm (RIGA). This algorithm uses Genetics Algorithms (GA) structures with features of retroviral replication, providing a great genetic diversity, confirmed by better results achieved by RIGA comparing with GA applied to some Real-Valued Benchmarking Functions.

Many researchers have controlled and analyzed biped robots that walk in the sagittal plane. These robots require the capability of walking merely laterally when they are faced with the obstacles such as a wall. In this field of study, both nonlinearity of the dynamic equations and also having a tracking system cause an effective control has to be utilized to address these problems. Therefore, this paper presents a nonlinear fuzzy tracking control for the walking robots that step in the lateral plane on a slopes. When fuzzy control is utilized to track the desired trajectories of the joints, there has to be a tradeoff between tracking errors and control efforts. Consequently, a particle swarm optimization algorithm is used to obtain the Pareto front of these non-commensurable objective functions to determine the fuzzy control parameters. In this paper, normalized summation of angle errors and normalized summation of control efforts are considered as the objective functions. These objective functions have to be minimized simultaneously. A vector which contains the control parameters is considered as the vector of selective parameters with positive constant values. The obtained Pareto front by the proposed multi-objective algorithm is compared with three prominent algorithms, modified NSGAII, Sigma method and MATLAB Toolbox MOGA. The result dramatizes the superiority of innovative particle swarm optimization over the algorithms.