Ali Mohammadi

Followers

Following

159

Co-author

Public Views

Ali Mohammadi received the B.Sc. Degree in Telecommunications Engineering in 2013 from the Quchan University of Technology, Quchan, Razavi Khorasan Province, Iran.
He received his M.Sc. and P.h.D. Degrees in Electrical and Computer Engineering from the University of Birjand, Birjand, South Khorasan Province, Iran, in 2015 and 2019, respectively.
He recently completed a Post-Doctoral research on Applied Artificial Intelligence at Isfahan University of Technology (IUT), Isfahan, Isfahan, Isfahan Province, Iran.
Currently, Dr. Mohammadi is an Assistant Professor in Department of Electrical & Computer Engineering at an University, Iran.
His research interests include Soft Computing, Artificial Intelligence, Bio-inspired Optimization, Engineering Optimization, Circuit Design, System Identification, Evolutionary Computation, multi-objective optimization, Heuristic and Metaheuristic Algorithms, Digital Filter Design, and Signal & Image processing.
Address: Iran

less

InterestsView All (19)

Uploads

Papers by Ali Mohammadi

Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)

Engineering Applications of Artificial Intelligence, 2023

Today, intelligent optimization has become a science that few researchers have not used in dealin... more Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: “Proceedings of the Congress” (33, 10.09%), “Applied Soft Computing” (19, 5.81%), “Expert Systems with Applications” (18, 5.51%), “Knowledge-Based Systems” (12, 3.67%), “Engineering Applications of Artificial Intelligence” (12, 3.67%), “Advances in Engineering Software” (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece.

Designing INS/GNSS integrated navigation systems by using IPO algorithms

Neural Computing and Applications, 2023

The application of soft computing techniques can be largely found in engineering sciences. These ... more The application of soft computing techniques can be largely found in engineering sciences. These include the design and optimization of navigation systems for use in land, sea, and air transportation systems. In this paper, an attempt is made to leverage on novel metaheuristic optimization approaches for designing integrated navigation systems. For this purpose, a simplified version of the inclined planes system optimization (called SIPO) algorithm alongside its two standard and modified versions are used in comparison with the two conventional methods of genetic algorithm and particle swarm optimization. Considerations are made on an INS/GNSS problem with IMU MEMS modules. Outputs are presented in terms of statistical and performance indicators, such as runtime, fitness, convergence, navigation accuracy (velocity, latitude, longitude, altitude, roll, pitch, yaw), and routing along with the ranking of algorithms. Competitive performance and relative superiority of the standard IPO over other methods in evaluating results have been confirmed. So that compared to other state-of-the-art algorithms (GA, PSO, IPO, and MIPO), the best runtime rank with a value of 6/4 by SIPO and the best performance rank of fitness, navigation accuracy for the two assumed IMU modules, and the total rank with values of 4/4, 149/60, 165/60, and 332/128 obtained by IPO, respectively.

بهره گیری از روش های هوشمند به منظور طراحی بهینه مقایسه گر CMOS

روشی جدید در تشخیص صرع از سیگنال EEG با استفاده از ترکیب طبقه¬بند شبکه عصبی MLP و الگوریتم ابتکاری IPO

عملکرد روشهای بهینهسازی هوشمند در مسائل شناسایی سیستم IIR

Nature-inspired metaheuristic search algorithms for optimizing benchmark problems: inclined planes system optimization to state-of-the-art methods

Archives of Computational Methods in Engineering, 2023

In the literature, different types of inclined planes system optimization (IPO) algorithms have b... more In the literature, different types of inclined planes system optimization (IPO) algorithms have been proposed and evaluated in various applications. Due to the large number of variants and applications, this work provides an overview of IPO’s state-of-the-art in terms of variants presented, applications, statistical evaluation, and analysis. In addition, the performance of IPO variants are evaluated and compared. The results are benchmarked against other algorithms. Final evaluation based on statistical analysis and a new and effective ranking methodology indicates the optimal performance and relative success of all IPO variants and their performance in comparison with other recent diverse metaheuristic search competitors, including reinforcement learning, evolution-based, swarm-based, physics-based, and human-based. The performance of IPO variants shown that the use of bio-operators to improve the standard version is more successful than other applied approaches. So that, the successful performance of SIPO + M with a minimum overall ranking of 0.73 has been ahead of all versions, and the complexity of IPO equations has also been led to a high time loss and achieving a maximum overall ranking of 2.07. Among other algorithms, it shown that versions without control parameters perform exploration and exploitation processes intelligently and more successful. For example, POA-I, POA-II, SLOA, OPA, and CMBO are among the methods that achieved the best performance, with minimum overall ranking values of 0.363, 0.384, 0.387, 0.424, and 0.933, respectively.

Improving the Performance of Analog Integrated Circuits using Multi-Objective Metaheuristic Algorithms

2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019

Low-Noise Amplifier (LNA) is the first critical component and an important part of the analog int... more Low-Noise Amplifier (LNA) is the first critical component and an important part of the analog integrated systems and wireless communication technology. LNA plays a key role in the design of Radio Frequency (RF) circuits. High voltage gain, low power consumption, high bandwidth and low Noise Figure (NF) are among the most prominent characteristics of LNAs. In this paper, in order to establish an appropriate tradeoff between circuit contradictory objectives and overcoming the design problem of an efficient LNA, the approach is focused on utilizing metaheuristic optimization methods for elements intelligent sizing and circuit automatic design. For this purpose, the Computer-Aided Design (CAD) tool based on the new and powerful version of Multi-Objective Gray Wolf Optimization (MOGWO) has been used. Implementation of algorithms in Matlab and circuit simulations in Hspice has done. Simulation results, in contrast to other research, not only meet the design specifications, but also provide a variety of solutions under the "Pareto-optimality", which allows designers to have more design options. Also, the evaluations indicate the close competition between the proposed method and other commonly used methods.

A Novel Solution based on Multi-Objective AI Techniques for Optimization of CMOS LC_VCOS

Journal of Telecommunication, Electronic and Computer Engineering, 2015

A method of optimizing components and transistors sizing for CMOS Cross-Coupled LC voltage contro... more A method of optimizing components and transistors sizing for CMOS Cross-Coupled LC voltage controlled oscillators is presented in this paper. The design constrains of power consumption, phase noise, and the Figure of Merit (FoM) of LC_VCOs are applied on Multi-Objective AI techniques, simultaneously. The design parameters of LC_VCOs are obtained from the two strong algorithms, the Multi-Objective Inclined Planes system Optimization (MOIPO) and the Multi-Objective Particle Swarm Optimization (MOPSO). It was implemented in MATLAB, to the Pareto Optimal Front (POF) solutions, which have an amazingly trade-off between three objective functions. The LC_VCO circuits were simulated using this method in a 0.18μm-CMOS process by HSPICE RF environment. The results show that the size of components of the PMOS-only and NMOS-only integrated LC_VCOs and the optimal trade-off curve between minimum power and minimum phase noise.

Download

A Simplified and Efficient Version of Inclined Planes system Optimization Algorithm

2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), 2019

The efficiency of heuristic optimization methods has led researchers to considerably use these me... more The efficiency of heuristic optimization methods has led researchers to considerably use these methods to solve complex engineering problems. Inclined planes system optimization (IPO) is a relatively new heuristic algorithm that is inspired by the dynamic movement of objects on inclined planes without friction. This paper presents a simplified and effective version of IPO (called SIPO) with the aim of simplifying the main IPO equations, creating a powerful trade-off between the concepts of exploitation and exploration, and modifying the complexity of their structural parameters. The performance of the proposed algorithm is measured on 10 benchmark functions. The results clearly demonstrate the improved performance and superiority of SIPO compared to the standard version of the IPO and its improved versions.

Design and modeling of adaptive IIR filtering systems using a weighted sum - variable length particle swarm optimization

Applied Soft Computing, 2021

Abstract The use of optimization algorithms for designing Infinite Impulse Response (IIR) filters... more Abstract The use of optimization algorithms for designing Infinite Impulse Response (IIR) filters has been considered in many studies. The concern in this area is the multimodal error surface of such filters and their fitting with filter coefficients. The order of the modeled system has a direct effect on the number of coefficients, complexities of the error surface, and the filter’s stability. This paper proposes an efficient approach based on a variable length particle swarm optimization algorithm with a weighted sum fitness function (WS-VLPSO). The proposed WS-VLPSO is utilized as an effective adaptive algorithm for designing optimal IIR filters. The approach is based on the inclusion of the order as a discrete variable in the particle vector, which is done with the goal of intelligent minimizing of order and thereby reducing the design complexity of IIR filters. To ensure the optimality of the systems, the objective function is considered as a weighted sum. Also, a new criterion called Optimum Modeling Indicator (OMI) is introduced, a measure to determine the percentage reduction of order and the success rate of the proposed approach. The proposed algorithm is also applied for solving the sensor coverage problem as another real-world variable length engineering optimization application. Evaluation of simulation results, with Monte-Carlo simulation, indicates the acceptable improvement of identified structures and the significant performance of the proposed approaches. Note that the source codes of the paper will be publicly available at https://github.com/ali-ece .

Infinite impulse response systems modeling by artificial intelligent optimization methods

Evolving Systems, 2018

Artificial Intelligent Optimization (AIO) algorithms learn from the past searches via using a gro... more Artificial Intelligent Optimization (AIO) algorithms learn from the past searches via using a group of individuals or agents. These Artificial Intelligence-based optimizing techniques are able to solve complex optimization problems with complicated constraints. They find the optimal in the low possible number of iterations, where optimal means the best from all possibilities selected from a special point of view. This paper presents a research on employing AIO methods with aim to Infinite Impulse Response (IIR) system modeling for design and optimization of IIR digital filters. The proposed methods cover a variety of AIO methods; algorithm based on evolution strategy (genetic algorithm) and heuristic algorithms (particle swarm optimization, population-based; gravitational search algorithm, and inclined planes system optimization, both populationbased and based on Newton's laws). In this paper, the IIR system modeling is solved as a constrained single-objective optimization problem in the Mean Squared Error (MSE) fitness function and is evaluated for two different benchmark IIR plants with high and low orders. To evaluate performance, efficiency and efficacy of the methods, two important criteria are used: "Indicator of Success (IoS)" and "Degree of Reliability (DoR)". In addition, the effect of decreasing population size (search agents) is analyzed on the performance and efficiency of the algorithms. Simulation results clarify the success of the research in terms of the MSE, IoS and DoR. Keywords Artificial intelligent optimization • IIR system modeling • Digital filter design • Indicator of success • Degree of reliability.

Download

Design of optimal CMOS ring oscillator using an intelligent optimization tool

Soft Computing, 2017

This paper presents an intelligent sizing method to improve the performance and efficiency of a C... more This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS ring oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect trade-off between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillation frequency, the Power Consumption, Phase Noise, Figure of Merit, Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e., Overall Nondominated Vector Generation, and Spacing are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm, Multi-Objective Inclined Planes system Optimization, Multi-Objective Particle Swarm Optimization and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Based on our extensive simulations and experimental results MOMIPO outperforms the Communicated by V. Loia.

Download

Inclined planes system optimization algorithm for IIR system identification

International Journal of Machine Learning and Cybernetics, 2016

In this paper the digital infinite impulse response (IIR) filter design is modeled as an optimiza... more In this paper the digital infinite impulse response (IIR) filter design is modeled as an optimization problem. A new design method based on inclined planes system optimization (IPO) is introduced for the IIR system identification. IPO is a heuristic technique based on the dynamics of sliding motion along a frictionless inclined surface that has been demonstrated the reliable performance in solving of engineering complex problems. The effectiveness of the proposed method is verified in presence of the additive noise. In this work, both actual and reduced order identification of few benchmarked IIR plants is carried out in the simulations. Newton's Mechanics-based, swarm intelligence based and conventional evolutionary algorithms are used to model the same examples and simulation results are evaluated. The final results clearly demonstrate the good performance and premier identification of the proposed method along with well-tuned other algorithms.

Download

Analysis of swarm intelligence and evolutionary computation techniques in IIR digital filters design

2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), 2016

Digital filters provide excellent advantages, compared to analog filters, such as better stabilit... more Digital filters provide excellent advantages, compared to analog filters, such as better stability and precision. According to the duration/length of the impulse response, digital filters are categorized as Finite-Impulse-Response (FIR) and Infinite-Impulse-Response (IIR) filters. Because the error surface of IIR filters is mostly multimodal, powerful global optimization techniques are preferred for avoid local minima in the filter design process. Artificial Intelligence (AI)-based approaches, Swarm Intelligence (SI) and Evolutionary Computation (EC) techniques are candidate methods to address this problem and to produce desirable solutions. SI is used to model the collective behavior of social swarms in nature, such as ant colonies, honey bees, and bird flocks. The EC is based on the principle of evolution (survival of the fittest). In this paper, a novel index for IIR filter design is introduced (called "Indicator of Success") and SI and EC algorithms are tested and evaluated for several numbers of novel and conventional heuristic algorithms. The reduced-order identification of two benchmarked IIR plants are carried out. We analyzed the performance of the proposed algorithms in IIR digital filters design in terms of the reliability, Mean-Square-Error (MSE) and IoS. The results demonstrate the proper and reliable performance of the SI algorithms compared to that achieved by EC algorithms.

IIR model identification using a modified inclined planes system optimization algorithm

Artificial Intelligence Review, 2016

Inclined planes system optimization (IPO) is a new optimization algorithm inspired by the sliding... more Inclined planes system optimization (IPO) is a new optimization algorithm inspired by the sliding motion dynamic along a frictionless inclined surface. In this paper, with the aim of create a powerful trade-off between the concepts of exploitation and exploration, and rectify the complexity of their structural parameters in the standard IPO, a modified version of IPO (called MIPO) is introduced as an efficient optimization algorithm for digital infinite-impulse-response (IIR) filters model identification. The IIR model identification is a complex and practical challenging problem due to multimodal error surface entanglement that many researches have been reported for it. In this work, MIPO utilizes an appropriate mechanism based on the executive steps of algorithm with the constant damp factors. To do this, unknown filter parameters are considered as a vector to be optimized. In implementation, at first, to demonstrate the effectiveness of the proposed method, 10 well-known benchmark functions have been considered for evaluating and testing. In addition, statistical analysis on the powerfulness, efficiency and applicability of the MIPO algorithm are presented. Obtained results in compared to some other popular methods, confirm the efficiency of the MIPO algorithm that makes the best optimal solutions and has a better performance and acceptable solutions.

Download

Inclined planes system optimization: theory, literature review, and state-of-the-art versions for IIR system identification

Expert Systems with Applications, 2022

The Inclined Planes System Optimization (IPO) algorithm is recent algorithm that uses Newton’s se... more The Inclined Planes System Optimization (IPO) algorithm is recent algorithm that uses Newton’s second law to perform optimization. After conducting a thorough literature review, this paper proposes an improved version of IPO called IIPO. This improvement is achieved by changing exploratory and exploitative behavior of the standard IPO proportional to the progress of optimization (iteration). The IIPO is employed for optimizing IIR digital filter design, which is a challenging engineering problem. Adaptive IIR modeling as a multimodal optimization problem is designed and developed under system identification structure with an appropriate single-objective function in the frequency domain. Implementations are performed in both modeling cases with same and reduced orders, and under two identification forms with and without environmental additive noise. The results are reported along with various analyzes compared to a wide range of IPO variants. The statistical results on 100 independent trials show a success of more than 90% of cases, the proposed IIPO algorithm substantially outperforms other comparative algorithms in terms of accuracy of estimated coefficients, convergence, fitness, output responses, noise analysis, stability, and reliability.

Download

Design and modeling of adaptive IIR filtering systems using a weighted sum - variable length particle swarm optimization

Applied Soft Computing, 2021

Design and modeling of adaptive IIR filtering systems using a weighted sum - variable length part... more

Download

Infinite impulse response systems modeling by artificial intelligent optimization methods

Evolving Systems, 2019

Artificial Intelligent Optimization (AIO) algorithms learn from the past searches via using a gro... more Artificial Intelligent Optimization (AIO) algorithms learn from the past searches via using a group of individuals or agents. These Artificial Intelligence-based optimizing techniques are able to solve complex optimization problems with complicated constraints. They find the optimal in the low possible number of iterations, where optimal means the best from all possibilities selected from a special point of view. This paper presents a research on employing AIO methods with aim to Infinite Impulse Response (IIR) system modeling for design and optimization of IIR digital filters. The proposed methods cover a variety of AIO methods; algorithm based on evolution strategy (genetic algorithm) and heuristic algorithms (particle swarm optimization, population-based; gravitational search algorithm, and inclined planes system optimization, both population-based and based on Newton’s laws). In this paper, the IIR system modeling is solved as a constrained single-objective optimization problem in the Mean Squared Error (MSE) fitness function and is evaluated for two different benchmark IIR plants with high and low orders. To evaluate performance, efficiency and efficacy of the methods, two important criteria are used: “Indicator of Success (IoS)” and “Degree of Reliability (DoR)”. In addition, the effect of decreasing population size (search agents) is analyzed on the performance and efficiency of the algorithms. Simulation results clarify the success of the research in terms of the MSE, IoS and DoR.

Design of optimal CMOS ring oscillator using an intelligent optimization tool

by Ali Mohammadi and Hamid Zahiri

Inclined planes system optimization algorithm for IIR system identification

In this paper the digital infinite impulse response (IIR) filter design is modeled as an optimiza... more In this paper the digital infinite impulse response (IIR) filter design is modeled as an optimization problem. A new design method based on inclined planes system optimization (IPO) is introduced for the IIR system identification. IPO is a heuristic technique based on the dynamics of sliding motion along a frictionless inclined surface that has been demonstrated the reliable performance in solving of engineering complex problems. The effectiveness of the proposed method is verified in presence of the additive noise. In this work, both actual and reduced order identification of few benchmarked IIR plants is carried out in the simulations. Newton’s Mechanics-based, swarm intelligence based and conventional evolutionary algorithms are used to model the same examples and simulation results are evaluated. The final results clearly demonstrate the good performance and premier identification of the proposed method along with well-tuned other algorithms.

Ali Mohammadi

Uploads

Papers by Ali Mohammadi

Log In