Improved Particle Swarm Optimization with low-discrepancy sequences

Pant, Millie; Thangaraj, Radha; Groşan, Crina; Abraham, Ajith

doi:10.1109/cec.2008.4631204

Cited by 37 publications

(26 citation statements)

References 13 publications

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“…In the previous studies, it has already been shown that the performance of Sobol sequence dominates the performance of Halton and Faure sequences. The performance of PSO using Van der Corput sequence called VCPSO along with PSO with Sobol sequence called SOPSO (which is said be superior than other low discrepancy sequences according to the previous studies) for swarm initialization is scrutinized and tested them for solving global optimization problems in large dimension search spaces by Pant et al [19]. …”

Section: Initializing the Swarm Using Low-discrepancy Sequences [19]mentioning

confidence: 99%

Particle Swarm Optimization: Performance Tuning and Empirical Analysis

Pant

Thangaraj

Abraham

2009

Foundations of Computational Intelligence Volume 3

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Abstract. This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.

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Section: Initializing the Swarm Using Low-discrepancy Sequences [19]mentioning

confidence: 99%

Particle Swarm Optimization: Performance Tuning and Empirical Analysis

Pant

Thangaraj

Abraham

2009

Foundations of Computational Intelligence Volume 3

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“…Some well known QRS are: Vander Corput, Sobol, Faure and Halton. These sequences have been applied to initialize the swarm and the numerical results show a marked improvement over the traditional BPSO, which uses uniformly, distributed random numbers [4], [5]. QRS are said to be better than pseudo random sequences, because of their ability to cover the search space more evenly in comparison to pseudo random sequences (see Figures 1 and 2).…”

Section: Quasi Random Sequences (Qrs)mentioning

confidence: 99%

Particle Swarm Optimization Using Sobol Mutation

Pant

Thangaraj

Singh

et al. 2008

2008 First International Conference on Emerging Trends in Engineering and Technology

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“…Parsopoulos and Varahatis (2002) use the Nonlinear Simplex method to initialize the particle swarm. LowDiscrepancy Sequences for sampling, such as Faure Sequence, Halton Sequence, Sobol Sequence and Vander Corput Sequence have been used to generate initial samples to form the particle swarm (Brits 2002;Uy et al 2007;Pant et al 2008). Pant et al compare the probability distribution (Gaussian, Exponential, and Lognormal) for obtaining the random numbers with the classical uniform random sampling method, and conclude that the former leads to better results (Krohling and Coelho 2006;Krohling 2004;Pant et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Improved particle swarm optimization algorithm using design of experiment and data mining techniques

Liu

Zhu

Chen

et al. 2015

Struct Multidisc Optim

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Particle swarm optimization (PSO) is a relatively new global optimization algorithm. Benefitting from its simple concept, fast convergence speed and strong ability of optimization, it has gained much attention in recent years. However, PSO suffers from premature convergence problem because of the quick loss of diversity in solution search. In order to improve the optimization capability of PSO, design of experiment method, which spreads the initial particles across a design domain, and data mining technique, which is used to identify the promising optimization regions, are studied in this research to initialize the particle swarm. From the test results, the modified PSO algorithm initialized by OLHD (Optimal Latin Hypercube Design) technique successfully enhances the efficiency of the basic version but has no obvious advantage compared with other modified PSO algorithms. An extension algorithm, namely OLCPSO (Optimal Latin hypercube design and Classification and Regression tree techniques for improving basic PSO), is developed by consciously distributing more particles into potential optimal regions. The proposed method is tested and validated by benchmark functions in contrast with the basic PSO algorithm and five PSO variants. It is found from the test studies that the OLCPSO algorithm successfully enhances the efficiency of the basic PSO and possesses competitive optimization ability and algorithm stability in contrast to the existing initialization PSO methods.

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Improved Particle Swarm Optimization with low-discrepancy sequences

Cited by 37 publications

References 13 publications

Particle Swarm Optimization: Performance Tuning and Empirical Analysis

Particle Swarm Optimization: Performance Tuning and Empirical Analysis

Particle Swarm Optimization Using Sobol Mutation

Improved particle swarm optimization algorithm using design of experiment and data mining techniques

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