A Modification of the Imperialist Competitive Algorithm With Hybrid Methods for Constrained Optimization Problems

Luo, Jianfu; Zhang, Jinsheng; Jiang, Xi

doi:10.1109/access.2021.3133579

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…The initial diversity of the population can effectively expand the search range of the algorithm and improve the optimization level and convergence speed of the algorithm [71,72]. In the standard BWOA, the random initial population position would cause the uneven position distribution of black widows, which will affect the development ability of the algorithm.…”

Section: A Population Initialization Through Double Chaotic Mappingmentioning

confidence: 99%

An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

Yin

2023

IEEE Access

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In solving engineering constrained optimization problems, the conventional black widow optimization algorithm (BWOA) has some shortcomings such as insufficient robustness and slow convergence speed. Therefore, an improved black widow optimization algorithm (IBWOA) is proposed by combining methods of double chaotic map, Cauchy center of gravity inverse difference mutation and golden sine guidance strategy. Firstly, the quality of the initial population of the BWOA is improved based on the double chaotic map; Secondly, in order to make full use of the difference information between the current and the optimal position thus improve optimization accuracy, the golden sine algorithm (Gold-SA) is introduced to update the position of the black widow individuals; Finally, the Cauchy barycenter reverse differential mutation operator is employed to increase the diversity of the population, avoid local optimization thus improve the global search ability of the algorithm. In addition, the global convergence characteristics of the IBWOA are analyzed based on the Markov process and the convergence probability reaches 1 for the globally optimal solution. The performance of the proposed IBWOA was evaluated based on eight continuous / discrete hybrid engineering optimization problems and typical benchmark functions. The results show that the improved BWOA can improve the search accuracy, convergence speed and robustness effectively comparing with some other conventional optimization algorithms.INDEX TERMS Black widow optimization algorithm, double chaotic map, golden sine algorithm (Gold-SA), Cauchy barycentric reverse difference mutation operator, Markov chain, engineering optimization.

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Section: A Population Initialization Through Double Chaotic Mappingmentioning

confidence: 99%

An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

Yin

2023

IEEE Access

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“…One such algorithm is the Imperialist Competitive Algorithm (ICA), which was developed by Gargari et al [30]. This is a new meta-heuristic optimization algorithm that incorporates political and social evolution [32]. The algorithm classifies the most powerful nations as imperialists and the rest as colonies.…”

Section: Optimization Of Process Parameters (1) Optimization By Icamentioning

confidence: 99%

“…θ and x are uniformly distributed random numbers. β is a number greater than one, d represents the distance between the imperialists and colonies, and γ is a parameter that determines the extent of deviation from the original direction [28,[30][31][32].…”

Section: Optimization Of Process Parameters (1) Optimization By Icamentioning

confidence: 99%

“…Several evolutionary algorithms have been developed to address optimization problems across a range of scientific and engineering disciplines. These algorithms include the Genetic Algorithm (GA) [23][24][25][26][27], the ant colony optimization algorithm, the bee algorithm, and the Imperialist Competitive Algorithm (ICA) [28][29][30][31][32]. These algorithms establish robust process parameter combinations for achieving superior joint quality.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Algorithm to Optimize Process Parameters of Al/Steel Magnetic Pulse Welding

Shim,

Kim

2023

Applied Sciences

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The Magnetic Pulse Welding (MPW) process uses only electromagnetic force to create a solid-state metallurgical bond between a working coil and outer workpiece. The electromagnetic force drives the outer tube to collide with the inner rod, resulting in successful bonding. However, due to the dissimilarity of the MPW joint, only a portion of the interface forms a metallurgical bond, which affects the quality of the joint. Therefore, the purpose of this study is to analyze the effects of process parameters on joint quality through experimental work using RSM. Furthermore, an optimization algorithm is utilized to optimize the process parameters used in magnetic pulse welding. A1070 aluminum and S45C carbon steel were used as the materials, while peak current, gap between working coil and outer tube, and frequency were chosen as the process parameters for MPW. The welding conditions are determined through experimental design. After welding, the maximum load and weld length are measured to analyze the effect of the process parameters, and a prediction model is developed. Specifically, to achieve a high-quality joint, the process parameters are optimized using the Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA). The results reveal that the peak current is a significant parameter, and the developed prediction model exhibits high accuracy. Furthermore, the ICA algorithm proves very effective in determining the process parameters for achieving a high-quality Al/Steel MPW joint.

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“…We observed that currently in the scientific literature, the promising evolutionary algorithm for optimization inspired by imperialistic competition (ICA) still has not been used to design and optimize analog and RF CMOS ICs with robustness analyses in the optimization loop; however, it has been used very successfully to solve many optimization problems in other engineering areas [22][23][24]. Therefore, the motivation of this study is to propose a customized imperialist competitive algorithm (ICA) to design and optimize robust Miller CMOS OTAs with two different bulk CMOS IC manufacturing processes from the Taiwan Semiconductor Company (TSMC) (180 nm and 65 nm nodes).…”

Section: Introductionmentioning

confidence: 99%

Customized Imperialist Competitive Algorithm Methodology to Optimize Robust Miller CMOS OTAs

Galembeck¹,

Gimenez

Moreto³

2022

Electronics

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The design and optimization of the analog complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) are intrinsically complicated and depend heavily on the designer’s experience, and are associated with very long design and optimization-cycle times. In addition, in order to the analog and radiofrequency (RF) CMOS IC work suitably in practice, it is necessary to perform robustness analyses (RAs) through Simulation Program with Integrated Circuit Emphasis (SPICE) simulations, which result in still-higher design and optimization cycle times and therefore represent the biggest bottleneck to the launching of new electronic products. In this context, this manuscript aims to present, for the first time, the use of a custom imperialist competitive algorithm (ICA) in order to reduce the design and optimization-cycle times of analog CMOS ICs. In this study, we implement some Miller CMOS operational transconductance amplifiers (OTAs) using the computational tool named iMTGSPICE, considering two different bulk CMOS IC manufacturing processes from Taiwan Semiconductor Company (TSMC) (180 nm and 65 nm nodes) and two evolutionary optimization methodologies of artificial intelligence, i.e., ICA and a genetic algorithm (GA). The main result obtained by this work shows that, by using an ICA-customized evolutionary algorithm to perform the design and optimization processes of Miller CMOS OTAs, it is possible to reduce the design and optimization-cycle times by up to 83% in relation to those implemented with the GA-customized evolutionary algorithm, achieving practically the same electrical performance.

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A Modification of the Imperialist Competitive Algorithm With Hybrid Methods for Constrained Optimization Problems

Cited by 11 publications

References 36 publications

An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

Evolutionary Algorithm to Optimize Process Parameters of Al/Steel Magnetic Pulse Welding

Customized Imperialist Competitive Algorithm Methodology to Optimize Robust Miller CMOS OTAs

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