Efficient Initialization of Particle Swarm Optimization Using Low Discrepancy Sequence

Gupta, Shubham; Gupta, Himanshu; Arora, Sagar; Nayak, Pranshu; Shrivastava, Tanmay

doi:10.1007/978-3-319-60618-7_43

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Chengwei Yang [16] introduced a novel initialization approach QRS and time-varying inertia weight, titled LHNPSO, for non-linear high-order functions. The successful initialization of PSO using the Quasirandom Sobol and Halton sequences was validated by Shubham et al [17].…”

Section: Literature Reviewmentioning

confidence: 93%

Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Ashraf¹,

Almazroi²,

Bangyal³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Particle Swarm Optimization (PSO) is a well-known extensively utilized algorithm for a distinct type of optimization problem. In meta-heuristic algorithms, population initialization plays a vital role in solving the classical problems of optimization. The population's initialization in meta-heuristic algorithms urges the convergence rate and diversity, besides this, it is remarkably beneficial for finding the efficient and effective optimal solution. In this study, we proposed an enhanced variation of the PSO algorithm by using a quasi-random sequence (QRS) for population initialization to improve the convergence rate and diversity. Furthermore, this study represents a new approach for population initialization by incorporating the torus sequence with PSO known as TO-PSO. The torus sequence belongs to the family of low discrepancy sequence and it is utilized in the proposed variant of PSO for the initialization of swarm. The proposed strategy of population's initialization has been observed with the fifteen most famous unimodal and multimodal benchmark test problems. The outcomes of our proposed technique display outstanding performance as compared with the traditional PSO, PSO initialized with Sobol Sequence (SO-PSO) and Halton sequence (HO-PSO). The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the other classical approaches. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.

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Section: Literature Reviewmentioning

confidence: 93%

Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Ashraf¹,

Almazroi²,

Bangyal³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Measurable examinations are performed with standard capacity in high measurements to approve the productivity of the presented approach and union. In, [46] executed the low disparity Halton and Sobol successions to instate the PSO, just as an examination of the new form that is performed with standard PSO introduced by pseudo-random numbers. The thorough investigation presumed that PSO that is introduced by low inconsistent successions created effective outcomes than others.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

Pervaiz¹,

Bangyal²,

Ashraf³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Efficient Initialization of Particle Swarm Optimization Using Low Discrepancy Sequence

Cited by 2 publications

References 18 publications

Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Particle Swarm Optimization with New Initializing Technique to Solve Global Optimization Problems

Comparative Research Directions of Population Initialization Techniques using PSO Algorithm

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