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

Author

Listed:
  • Rahul Roy

    (KIIT University, India)

  • Satchidananda Dehuri

    (Fakir Mohan University, India)

  • Sung Bae Cho

    (Yonsei University, South Korea)

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.

Suggested Citation

  • Rahul Roy & Satchidananda Dehuri & Sung Bae Cho, 2011. "A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 2(4), pages 41-57, October.
  • Handle: RePEc:igg:jamc00:v:2:y:2011:i:4:p:41-57
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    Cited by:

    1. Anupam Mukherjee & Partha Sarathi Barma & Joydeep Dutta & Goutam Panigrahi & Samarjit Kar & Manoranjan Maiti, 2022. "A multi-objective antlion optimizer for the ring tree problem with secondary sub-depots," Operational Research, Springer, vol. 22(3), pages 1813-1851, July.
    2. Xinyu An & Baowei Song & Zhaoyong Mao & Congcong Ma, 2018. "Layout Optimization Design of Two Vortex Induced Piezoelectric Energy Converters (VIPECs) Using the Combined Kriging Surrogate Model and Particle Swarm Optimization Method," Energies, MDPI, vol. 11(8), pages 1-22, August.

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