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Title
Abstract
Introduction
Invasive Weed Optimization -An Overview
Adapting Iwo for Multi-Objective Optimization
Experimental Results
Conclusions
References
All Topics
Computer Science
Artificial Intelligence

Multi-objective optimization with artificial weed colonies

Profile image of Sayan GhoshSayan Ghosh

2011, Information Sciences

https://doi.org/10.1016/J.INS.2010.09.026
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14 pages

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Abstract

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving

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This paper presents a novel memetic algorithm, named as IWO_DE, to tackle constrained numerical and engineering optimization problems. In the proposed method, invasive weed optimization (IWO), which possesses the characteristics of adaptation required in memetic algorithm, is firstly considered as a local refinement procedure to adaptively exploit local regions around solutions with high fitness. On the other hand, differential evolution (DE) is introduced as the global search model to explore more promising global area. To accommodate the hybrid method with the task of constrained optimization, an adaptive weighted sum fitness assignment and polynomial distribution are adopted for the reproduction and the local dispersal process of IWO, respectively. The efficiency and effectiveness of the proposed approach are tested on 13 well-known benchmark test functions. Besides, our proposed IWO_DE is applied to four well-known engineering optimization problems. Experimental results suggest that IWO_DE can successfully achieve optimal results and is very competitive compared with other state-of-art algorithms.

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Hybrid invasive weed/biogeography-based optimization
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Engineering Applications of Artificial Intelligence, 2017

We propose a new variant of the ecologically-inspired optimization method known as invasive weed optimization (IWO). The proposed algorithm features three new components that are typically not present in IWO: (1) migration; (2) gradient descent; and (3) mutation. In standard IWO, each individual uses only its own features (that is, independent solution variables) to randomly distribute new seeds over the search space. In other words, there is no sharing of features among individuals. We propose the application of the migration operator from biogeography-based optimization (BBO) to include the feature-sharing capability in IWO. This modification improves the quality of the distributed seeds (that is, new candidate solutions) in the population. To further improve the local search ability of IWO, we propose the use of gradient descent. Mutation is activated under certain conditions to increase the diversity of the population, and escape local optima. We demonstrate the performance of this new hybrid IWO/BBO on a set of single-objective benchmarks, and on a real-world cyber-physical system problem to optimize a user intent recognition system for transfemoral amputees. Hybrid IWO/BBO is compared to standard IWO, BBO, and 10 other optimization algorithms. The Kruskal-Wallis and Wilcoxon signed-rank tests are used to statistically compare the algorithms. The results for hybrid IWO/BBO present promising improvements over standard IWO. For instance, out of 25 benchmarks, hybrid IWO/BBO performs better than IWO on 18 problems with dimension 30. Hybrid IWO/BBO shows competitive performance with comparison to the 10 other state-of-the-art optimization algorithms.

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A MODIFIED INVASIVE WEED OPTIMIZATION ALGORITHM FOR MULTIOBJECTIVE FLEXIBLE JOB SHOP SCHEDULING PROBLEMS
Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)

In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that’s why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

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This paper proposes a novel optimization methodology aimed at solving economic dispatch (ED) problem considering valve point effects using invasive weed optimization (IWO) algorithm. IWO was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. The proposed method has been applied to three test systems comprising 3, 13 and 40 units. These test systems are also solved using the catfish particle swarm optimisation (Catfish PSO) algorithm an improved version of PSO for the comparison and validation of the solutions. The obtained best values have been compared with different methods in literature and the results of them have been discussed. The result obtained proves that the IWO algorithm is efficient and can be used practically f...

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In this paper, a modified invasive weed optimization (IWO) algorithm is presented for optimization of multiobjective flexible job shop scheduling problems (FJSSPs) with the criteria to minimize the maximum completion time (makespan), the total workload of machines and the workload of the critical machine. IWO is a bio-inspired metaheuristic that mimics the ecological behaviour of weeds in colonizing and finding suitable place for growth and reproduction. IWO is developed to solve continuous optimization problems that's why the heuristic rule the Smallest Position Value (SPV) is used to convert the continuous position values to the discrete job sequences. The computational experiments show that the proposed algorithm is highly competitive to the state-of-the-art methods in the literature since it is able to find the optimal and best-known solutions on the instances studied.

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AN INVASIVE WEED OPTIMIZATION (IWO) APPROACH FOR MULTI-OBJECTIVE JOB SHOP SCHEDULING PROBLEMS (JSSPs
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In this paper, a new meta-heuristic solution approach for Multi-objective Job Shop Scheduling Problems (JSSP) is presented. The proposed algorithm makes use of Mehrabian & Lucas’s heuristic ‘Invasive Weed Optimization’ (IWO) in generating optimal schedules. For performance evaluation of solutions in a Multi-objective scenario, a concept called’ Fuzzy dominance’ has been employed. The results obtained from our study have shown that the proposed algorithm can be used as a new alternative solution technique for finding good solutions to the complex Multi-objective Job Shop Scheduling problems.

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Multimodal optimization by artificial weed colonies enhanced with localized group search optimizers
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Multimodal optimization aims at finding multiple global and local optima (as opposed to a single solution) of a function, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable one while still maintaining the optimal system performance. Evolutionary Algorithms (EAs) due to their population-based approach are able to detect multiple solutions within a population in a single simulation run and have a clear advantage over the classical optimization techniques, which need multiple restarts and multiple runs in the hope that a different solution may be discovered every run, with no guarantee however. This article proposes a hybrid two-stage optimization technique that firstly employs Invasive Weed Optimization (IWO), an ecologically inspired algorithm to find the promising Euclidean sub-regions surrounding multiple global and local optima. IWO is run for 80% of the total budget of function evaluations (FEs), and consecutively the search is intensified by using a modified Group Search Optimizer (GSO), in each detected sub-region. GSO, invoked in each sub-region discovered with IWO, is continued for 20% of the total budget of FEs. Both IWO and GSO have been modified from their original forms to meet the demands of the multimodal problems used in this work. Performance of the proposed algorithm is compared with a number of state-of-the-art multimodal optimization algorithms over a benchmark-suite comprising of 21 basic multimodal problems and 7 composite multimodal problems. A practical multimodal optimization problem concerning the design of dielectric composites has also been used to test the performance of the algorithm. Experimental results suggest that the proposed technique is able to provide better and more consistent performance over the existing well-known multimodal algorithms for majority of the test problems without incurring any serious computational burden.

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Application of the invasive weed optimization technique for antenna configurations
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In this paper a novel numerical optimization technique for antenna configurations is introduced. This algorithm is inspired from colonizing weeds, which is shown to be very robust and adaptive to changes in the environment. Thus, capturing their properties would lead to a powerful optimization algorithm. The feasibility, efficiency and effectiveness of the proposed algorithm for optimization of antenna problems are examined by a set of antenna configurations. The obtained results are compared with a particle swarm optimization technique which is widely used in antenna optimization. Numerical results show that there is a good agreement between the corresponding results.

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Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem
andrew frank

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The personalized urban multi-criteria quasi-optimum path problem (PUMQPP) is a branch of multicriteria shortest path problems (MSPPs) and it is classified as a NP-hard problem. To solve the PUMQPP, by considering dependent criteria in route selection, there is a need for approaches that achieve the best compromise of possible solutions/routes. Recently, invasive weed optimization (IWO) algorithm is introduced and used as a novel algorithm to solve many continuous optimization problems. In this study, the modified algorithm of IWO was designed, implemented, evaluated, and compared with the genetic algorithm (GA) to solve the PUMQPP in a directed urban transportation network. In comparison with the GA, the results have shown the significant superiority of the proposed modified IWO algorithm in exploring a discrete search-space of the urban transportation network. In this regard, the proposed modified IWO algorithm has reached better results in fitness function, quality metric and running-time values in comparison with those of the GA.

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Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets
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Multimodal optimization is used to find multiple global & local optima which is very useful in many real world optimization problems. But often evolutionary algorithms fail to locate multiple optima as required by the system. Also they fail to store those optima by themselves. So we have to use other selection scheme that can detect & store multiple optima along with evolutionary algorithms. Hence we use niching which is a very powerful tool in detecting & storing multiple optima. Niching methods were introduced to EAs to allow maintenance of a population of diverse individuals so that multiple optima within a single population can be located .Crowding which is a very primitive branch of niching is used here as the selection scheme with Invasive Weed Optimization (IWO) which is a ecologically inspired algorithms depicting behaviors of plants . For multimodal optimization the total search space is divided into several niches in which separately IWO is applied to find the optima in niches. The niches will also store this optima within themselves.

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