A Dynamic Chaotic Mutation Based Particle Swarm Optimization for Dynamic Optimization of Biochemical Process

Wang, Kangtai; Li, Fupeng

doi:10.1109/icisce.2017.168

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Tholudur and Ramirez [51] obtained a value of 32.47 using a modified iterative dynamic programming algorithm (FIDP). Wang and Li [52] obtained values 32.68851327 (N = 10) and 32.68851335 (N = 20) using a dynamic chaotic mutation based particle swarm optimization (DCM-PSO). tain gap between MSFO, IKBCA, and VSACS.…”

Section: Analysis Of the Experimental Results Of Casementioning

confidence: 99%

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

Zhang

2021

Processes

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The optimal solution of the chemical dynamic optimization problem is the basis of automatic control operation in the chemical process, which can reduce energy consumption, increase production efficiency, and maximize economic benefit. In this paper, a modified sailfish optimizer (MSFO) combined with an equal division method is proposed for solving chemical dynamic optimization problems. Based on the basic sailfish optimizer, firstly, the tent chaotic mapping strategy is introduced to disturb the initialization of sailfish and sardine populations to avoid the loss of population diversity. Secondly, an adaptive linear reduction strategy of attack parameters is proposed to enhance the exploration and exploitation ability of sailfish. Thirdly, the updating formula of sardine position is modified, and the global optimal solution is used to attract all sardine positions, which can avoid the premature phenomenon of the algorithm. Eventually, the MSFO is applied to solve six classical optimization cases of chemical engineering to evaluate its feasibility. The experimental results are analyzed and compared with other optimization methods to prove the superiority of the MSFO in solving chemical dynamic optimization problems.

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Section: Analysis Of the Experimental Results Of Casementioning

confidence: 99%

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

Zhang

2021

Processes

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“…PSO-IM outperforms the original PSO in all test cases. K. Wang and F. Li [24] brought dynamic chaotic behavior in PSO called (dcmPSO) to gain global exploration at the start of iteration and local exploitation at the end of the iteration. The logistic map turns the mutation process into chaotic state.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm with Chi-Square Mutation Strategy

Bangyal¹,

Tayyab²,

Batool³

et al. 2019

ijacsa

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Particle Swarm Optimization (PSO) algorithm is a population-based strong stochastic search strategy empowered from the inherent way of the bee swarm or animal herds for seeking their foods. Consequently, flexibility for the numerical experimentation, PSO has been used to resolve diverse kind of optimization problems. PSO is much of the time caught in local optima in the meantime taking care of the complex real-world problems.Considering this, a novel modified PSO is introduced by proposing a chi square mutation method. The main functionality of mutation operator in PSO is quick convergence and escapes from the local minima. Population initialization plays a critical role in meta-heuristic algorithm. Moreover, in this work, to improve the convergence, rather applying random distribution for initialization, two quasi random sequences Halton and Sobol have been applied and properly joined with chi-square mutated PSO (Chi-Square PSO) algorithm. The promising experimental result suggests the superiority of the proposed technique. The results present foresight that how the proposed mutation operator influences on the value of cost function and divergence. The proposed mutated strategy is applied for eight (8) benchmark functions extensively used in the literature. The simulation results verify that Chi-Square PSO provide efficient results over other tested algorithms implemented for the function optimization.

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An Overview of Mutation Strategies in Particle Swarm Optimization

Bangyal

Ahmad

Rauf

2020

International Journal of Applied Metaheuristic Computing

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The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

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A Dynamic Chaotic Mutation Based Particle Swarm Optimization for Dynamic Optimization of Biochemical Process

Cited by 3 publications

References 14 publications

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

Dynamic Optimization of Chemical Processes Based on Modified Sailfish Optimizer Combined with an Equal Division Method

An Improved Particle Swarm Optimization Algorithm with Chi-Square Mutation Strategy

An Overview of Mutation Strategies in Particle Swarm Optimization

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