Solving systems of unconstrained equations using particle swarm optimization

Brits, R.; Engelbrecht, Andries P.; Bergh, F. van den

doi:10.1109/icsmc.2002.1176019

Cited by 63 publications

(33 citation statements)

References 5 publications

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“…Some previous instances where low discrepancy sequences have been used to improve the performance of optimization algorithms include [6,7,8,9,10]. Kimura and Matsumura [6] have used Halton sequence for initializing the Genetic Algorithms (GA) population and have shown that a real coded GA performs much better when initialized with a quasi random sequence in comparison to a GA which initialized with a population having uniform probability distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Kimura and Matsumura [6] have used Halton sequence for initializing the Genetic Algorithms (GA) population and have shown that a real coded GA performs much better when initialized with a quasi random sequence in comparison to a GA which initialized with a population having uniform probability distribution. Instances where quasi random sequences have been used for initializing the swarm in PSO can be found in [7,8,9,10]. In [8,9,10] authors have made use of Sobol and Faure sequences.…”

Section: Introductionmentioning

confidence: 99%

“…Instances where quasi random sequences have been used for initializing the swarm in PSO can be found in [7,8,9,10]. In [8,9,10] authors have made use of Sobol and Faure sequences. Similarly, Nguyen et al [7] have shown a detailed comparison of Halton Faure and Sobol sequences for initializing the swarm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Particle Swarm Optimization with low-discrepancy sequences

Pant

Thangaraj

Groşan

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Particle Swarm Optimization with low-discrepancy sequences

Pant

Thangaraj

Groşan

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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“…The issue of specifying several user parameters still remains. The authors also proposed nbest PSO in [9], where a particle's neighbourhood best is defined as the average of the positions of all particles in its neghbourhood. By computing the Euclidean distances between particles, the neighbourhood of a particle can be defined by its k closest particles, where k is a user-specified parameter.…”

Section: Nichepsomentioning

confidence: 99%

Developing Niching Algorithms in Particle Swarm Optimization

2011

Adaptation, Learning, and Optimization

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Abstract. Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing stateof-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algorithms and how they combat these problems by taking advantages of the unique characteristics of PSO. This chapter will also describe a recently proposed lbest ring topology based niching PSO. Our experimental results suggest that this lbest niching PSO compares favourably against some existing PSO niching algorithms.

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“…The framework provides a basis for factoring out the commonalities, and applying various properties uniformly across all the classes of algorithms, even where they were previously thought particular to one class (section 3). 2 In section 4 we describe our proof-of-concept prototype implementation of the generic framework on a platform of multiple field programmable gate array (FPGA) chips. Thus the generic architecture naturally permits both parallelism (multiple individuals executing on a single chip) and distribution (multiple individuals executing across the array of chips) of the algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Generic Framework for Population-Based Algorithms, Implemented on Multiple FPGAs

Newborough

Stepney

2005

Lecture Notes in Computer Science

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Abstract. Many bio-inspired algorithms (evolutionary algorithms, artificial immune systems, particle swarm optimisation, ant colony optimisation, …) are based on populations of agents. Stepney et al [2005] argue for the use of conceptual frameworks and meta-frameworks to capture the principles and commonalities underlying these, and other bio-inspired algorithms. Here we outline a generic framework that captures a collection of population-based algorithms, allowing commonalities to be factored out, and properties previously thought particular to one class of algorithms to be applied uniformly across all the algorithms. We then describe a prototype proof-of-concept implementation of this framework on a small grid of FPGA (field programmable gate array) chips, thus demonstrating a generic architecture for both parallelism (on a single chip) and distribution (across the grid of chips) of the algorithms.

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Solving systems of unconstrained equations using particle swarm optimization

Cited by 63 publications

References 5 publications

Improved Particle Swarm Optimization with low-discrepancy sequences

Improved Particle Swarm Optimization with low-discrepancy sequences

Developing Niching Algorithms in Particle Swarm Optimization

A Generic Framework for Population-Based Algorithms, Implemented on Multiple FPGAs

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