On the Hybridization of Particle Swarm Optimization Technique for Continuous Optimization Problems

The particle swarm optimization (PSO) method is becoming a popular optimizer within the mechanical design community because of its simplicity and ability to handle a wide variety of objective functions that characterize a proposed design. Typical examples arising in mechanical design are nonlinear objective functions with many constraints, which typically arise from the various design specifications. The method is particularly attractive to mechanical design because it can handle discontinuous functions that occur when the designer must choose from a discrete set of standard sizes. However, as in other optimizers, the method is susceptible to converging to a local rather than global minimum. In this paper, convergence criteria for the PSO method are investigated and an algorithm is proposed that gives the user a high degree of confidence in finding the global minimum. The proposed algorithm is tested against five benchmark optimization problems, and the results are used to develop specific guidelines for implementation.

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A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems

Applied Soft Computing ◽

10.1016/j.asoc.2012.12.007 ◽

2013 ◽

Vol 13 (4) ◽

pp. 2188-2203 ◽

Cited By ~ 92

Author(s):

Mustafa Servet Kıran ◽

Mesut Gündüz

Keyword(s):

Particle Swarm Optimization ◽

Artificial Bee Colony Algorithm ◽

Artificial Bee Colony ◽

Optimization Problems ◽

Particle Swarm ◽

Continuous Optimization ◽

Swarm Optimization ◽

Bee Colony ◽

Continuous Optimization Problems

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Using chaos enhanced hybrid firefly particle swarm optimization algorithm for solving continuous optimization problems

Sadhana ◽

10.1007/s12046-021-01572-w ◽

2021 ◽

Vol 46 (2) ◽

Author(s):

İbrahim Berkan Aydilek ◽

İzzettin Hakan Karaçizmeli ◽

Mehmet Emin Tenekeci ◽

Serkan Kaya ◽

Abdülkadir Gümüşçü

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Particle Swarm ◽

Continuous Optimization ◽

Swarm Optimization ◽

Continuous Optimization Problems

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Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

Discrete Dynamics in Nature and Society ◽

10.1155/2021/8378579 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Jing Li ◽

Yifei Sun ◽

Sicheng Hou

Keyword(s):

Experimental Study ◽

Particle Swarm Optimization ◽

Numerical Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Continuous Optimization ◽

Pso Algorithm ◽

Swarm Optimization ◽

Fixed Phase ◽

Continuous Optimization Problems

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

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