Hybrid charged system search and particle swarm optimization for engineering design problems

Purpose - Meta-heuristic methods are powerful in obtaining the solution of optimization problems. Hybridizing of the meta-heuristic algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to provide a new hybrid algorithm by adding positiv...

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Vydáno v:Engineering computations Ročník 28; číslo 4; s. 423 - 440
Hlavní autoři: Kaveh, A., Talatahari, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bradford Emerald Group Publishing Limited 01.01.2011
Témata:
Algorithms
Analysis
Charged particles
Computer programming
Design engineering
Electrical engineering
Exploitation
Heuristic
Heuristic methods
Optimization
Product development
Reagents
Searching
Studies
Violations
Electrical engineering
Programming and algorithm theory
Design and development
ISSN:0264-4401, 1758-7077
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Shrnutí:Purpose - Meta-heuristic methods are powerful in obtaining the solution of optimization problems. Hybridizing of the meta-heuristic algorithms provides a scope to improve the searching abilities of the resulting method. The purpose of this paper is to provide a new hybrid algorithm by adding positive properties of the particle swarm optimization (PSO) algorithms to the charged system search (CSS) to solve constrained engineering optimization problems.Design methodology approach - The main advantages of the PSO consisting of directing the agents toward the global best (obtained by the swarm) and the local best (obtained by the agent itself) are added to the CSS algorithm to improve its performance. In the present approach, similar to the original CSS, each agent is affected by other agents considering the governing laws of electrical physics. However, the kind of the forces can be repulsive and attractive. In order to handle the constraints, the fly-to-boundary method is utilized as an improved feasible-based method.Findings - Four variants of hybrid methods are proposed. In these algorithms, the charged memory (CM) is changed to save the local best positions of agents. Utilizing this new CM to determine the direction and amount of movement of agents improve the power of the algorithms. When only this memory is utilized (method I), exploitation ability of the algorithm increases and when only two agents from CM in addition to other agents in the current iteration are used, then the exploration ability increases (method II). In order to have a good balance between exploration and exploitation of the algorithms, methods III and IV are proposed, where some agents of the memory and some other from the current agents are utilized. Method IV in which the numbers of used agents from the CM increase linearly, has a better search ability in addition to a powerful exploitation making this variant superior compared to the others.Originality value - In this paper, four hybrid methods are presented and applied to some benchmark engineering optimization problems. The new algorithms are compared to those of the other advanced meta-heuristic methods to illustrate the effectiveness of the proposed methods.
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ISSN:0264-4401
1758-7077
DOI:10.1108/02644401111131876