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A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem

Rahul Roy, Satchidananda Dehuri, Sung Bae Cho
Copyright: © 2013 |Pages: 16
ISBN13: 9781466621459|ISBN10: 1466621451|EISBN13: 9781466621466
DOI: 10.4018/978-1-4666-2145-9.ch001
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MLA

Roy, Rahul, et al. "A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem." Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches, edited by Peng-Yeng Yin, IGI Global, 2013, pp. 1-16. https://doi.org/10.4018/978-1-4666-2145-9.ch001

APA

Roy, R., Dehuri, S., & Cho, S. B. (2013). A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem. In P. Yin (Ed.), Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches (pp. 1-16). IGI Global. https://doi.org/10.4018/978-1-4666-2145-9.ch001

Chicago

Roy, Rahul, Satchidananda Dehuri, and Sung Bae Cho. "A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem." In Trends in Developing Metaheuristics, Algorithms, and Optimization Approaches, edited by Peng-Yeng Yin, 1-16. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2145-9.ch001

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Abstract

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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