Using selection to improve particle swarm optimization

Angeline, Peter J.

doi:10.1109/icec.1998.699327

Cited by 570 publications

(292 citation statements)

References 2 publications

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“…Similar to this last one, the EPSO is an evolutionary approach that incorporates a selection procedure to the original PSO algorithm, as well as self-adapting properties for its parameters. This algorithm adds a tournament selection method used in evolutionary programming (EP) [20]. Based on the EPSO, a differential evolution operator has been proposed to improve the performance of the algorithm in two different ways.…”

Section: Algorithm 1 Traditional Pso Algorithmmentioning

confidence: 99%

Introducing the fractional-order Darwinian PSO

Couceiro

Rocha

Ferreira

et al. 2012

SIViP

138

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One of the most well-known bio-inspired algorithms used in optimization problems is the particle swarm optimization (PSO), which basically consists on a machinelearning technique loosely inspired by birds flocking in search of food. More specifically, it consists of a number of particles that collectively move on the search space in search of the global optimum. The Darwinian particle swarm optimization (DPSO) is an evolutionary algorithm that extends the PSO using natural selection, or survival of the fittest, to enhance the ability to escape from local optima. This paper firstly presents a survey on PSO algorithms mainly focusing on the DPSO. Afterward, a method for controlling the convergence rate of the DPSO using fractional calculus (FC) concepts is proposed. The fractional-order optimization algorithm, denoted as FO-DPSO, is tested using several well-known functions, and the relationship between the fractional-order velocity and the convergence of the algorithm is observed. Moreover, experimental results show that the FO-DPSO significantly outperforms the previously presented FO-PSO.

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Section: Algorithm 1 Traditional Pso Algorithmmentioning

confidence: 99%

Introducing the fractional-order Darwinian PSO

Couceiro

Rocha

Ferreira

et al. 2012

SIViP

138

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“…In order to test the influence of inertia weight on the PSO performance, three nonlinear benchmark functions reported in literature [15,16] were used since they are well known problems. The first function is the Rosenbrock function:…”

Section: Experimental Studiesmentioning

confidence: 99%

A New Approach to Improve Particle Swarm Optimization

Zhang

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Particle swarm optimization (PSO) is a new evolutionary computation technique. Although PSO algorithm possesses many attractive properties, the methods of selecting inertia weight need to be further investigated. Under this consideration, the inertia weight employing random number uniformly distributed in [0,1] was introduced to improve the performance of PSO algorithm in this work. Three benchmark functions were used to test the new method. The results were presented to show that the new method is effective.

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“…For a detailed description, the reader is suggested [2] - [7]. However, for selection and reproduction operator only a few examples are available (see for instance Angeline [8], Clerc [9]). The objective of this paper is to see the performance of a BPSO after including a crossover operator in it.…”

Section: Introductionmentioning

confidence: 99%

A New PSO Algorithm with Crossover Operator for Global Optimization Problems

Pant

Thangaraj

Abraham

2007

Advances in Soft Computing

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Abstract. This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases taken in this study.

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Using selection to improve particle swarm optimization

Cited by 570 publications

References 2 publications

Introducing the fractional-order Darwinian PSO

Introducing the fractional-order Darwinian PSO

A New Approach to Improve Particle Swarm Optimization

A New PSO Algorithm with Crossover Operator for Global Optimization Problems

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