An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications

The reliability–redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities...

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Published in:	Reliability engineering & system safety Vol. 94; no. 4; pp. 830 - 837
Main Author:	Coelho, Leandro dos Santos
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.04.2009 Elsevier
Subjects:	Applied sciences Evolutionary algorithm Exact sciences and technology Mathematical programming Meta-heuristics Operational research and scientific management Operational research. Management science Particle swarm optimization Reliability theory. Replacement problems Reliability–redundancy optimization PSO-CO PSO-GC Evolutionary algorithm PSO-CA Reliability–redundancy optimization Particle swarm optimization PSO Meta-heuristics Chaos Redundancy Gaussian distribution Mixed integer programming Global solution Reliability-redundancy optimization Vertebrata Social behavior Genetic algorithm Heuristic method Swarm intelligence Aves Reliability Metamodel
ISSN:	0951-8320, 1879-0836
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Abstract	The reliability–redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability–redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability–redundancy optimization problems. In this context, two examples in reliability–redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability–redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.
AbstractList	The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.
Author	Coelho, Leandro dos Santos
Author_xml	– sequence: 1 givenname: Leandro dos Santos surname: Coelho fullname: Coelho, Leandro dos Santos email: leandro.coelho@pucpr.br organization: Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Paraná, PUCPR, Imaculada Conceição, 1155, 80215-901 Curitiba, Paraná, Brazil
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Keywords	PSO-CO PSO-GC Evolutionary algorithm PSO-CA Reliability–redundancy optimization Particle swarm optimization PSO Meta-heuristics Chaos Redundancy Gaussian distribution Mixed integer programming Global solution Reliability-redundancy optimization Vertebrata Social behavior Genetic algorithm Heuristic method Swarm intelligence Aves Reliability Metamodel
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Snippet	The reliability–redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum... The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum...
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SubjectTerms	Applied sciences Evolutionary algorithm Exact sciences and technology Mathematical programming Meta-heuristics Operational research and scientific management Operational research. Management science Particle swarm optimization Reliability theory. Replacement problems Reliability–redundancy optimization
Title	An efficient particle swarm approach for mixed-integer programming in reliability–redundancy optimization applications
URI	https://dx.doi.org/10.1016/j.ress.2008.09.001 https://www.proquest.com/docview/34454434 https://www.proquest.com/docview/903631106
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