Particle Swarm Optimization Using Sobol Mutation

Pant, Millie; Thangaraj, Radha; Singh, V. P.; Abraham, Alice

doi:10.1109/icetet.2008.35

Cited by 23 publications

(8 citation statements)

References 6 publications

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“…SMPO was introduced by Millie Pant et al in 2008. They used a quasirandom Sobol sequence to initialize the particles instead of standard random numbers [25,32]. They used a new operator called a systematic mutation operator, which is used to improve the performance of the PSO.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

et al. 2021

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Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. These algorithms can influence the convergence to find an efficient optimal solution. Mainly, for recognizing the importance of diversity, several researchers have worked on the performance for the improvement of metaheuristic algorithms. Population initialization is a vital factor in metaheuristic algorithms such as PSO and DE. Instead of applying the random distribution for the initialization of the population, quasirandom sequences are more useful for the improvement the diversity and convergence factors. This study presents three new low-discrepancy sequences named WELL sequence, Knuth sequence, and Torus sequence to initialize the population in the search space. This paper also gives a comprehensive survey of the various PSO and DE initialization approaches based on the family of quasirandom sequences such as Sobol sequence, Halton sequence, and uniform random distribution. The proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO) and DE (DE-TO, DE-WE, and DE-KN) have been examined for well-known benchmark test problems and training of the artificial neural network. The finding of our techniques shows promising performance using the family of low-discrepancy sequences over uniform random numbers. For a fair comparison, the approaches using low-discrepancy sequences for PSO and DE are compared with the other family of low-discrepancy sequences and uniform random number and depict the superior results. The experimental results show that the low-discrepancy sequences-based initialization performed exceptionally better than a uniform random number. Moreover, the outcome of our work presents a foresight on how the proposed technique profoundly impacts convergence and diversity. It is anticipated that this low-discrepancy sequence comparative simulation survey would be helpful for studying the metaheuristic algorithm in detail for the researcher.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

et al. 2021

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“…SMPO was introduced by Millie Pant et al in 2008. They used quasi-random Sobol sequence to initialize the particles instead of normal random numbers [45]. They used a new operator called systematic mutation operator which is used to improve the performance of the PSO.…”

Section: Previous Workmentioning

confidence: 99%

“…The Sobol sequence is firstly introduced by a Russian mathematician, Sobol [45]. Then, reconstruct the coordinates.…”

Section: Sobolmentioning

confidence: 99%

Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences

et al. 2021

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To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail.

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“…Pant et al [14] gave a new mutation operator named as Sobol mutation operator that utilizes the quasi-random sequences to explore the search space better than random distribution. To keep the diversity in the swarm Zavala et al [15] gave two separate perturbation operators known as C-perturbation operator and M-perturbation operator and implemented to personal best position in lieu of perturbing the position vector of particle in the swarm.…”

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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Particle Swarm Optimization Using Sobol Mutation

Abstract: Abstract

Cited by 23 publications

References 6 publications

Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences

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

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