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A new orthogonal evolutionary algorithm based on decomposition for multi-objective optimization

Author

Listed:
  • Cai Dai

    (School of Computer Science and Technology, Xidian University, Xian, P.R. China)

  • Yuping Wang

    (School of Computer Science and Technology, Xidian University, Xian, P.R. China)

  • Wei Yue

    (School of Computer Science and Technology, Xidian University, Xian, P.R. China)

Abstract

The diversity of solutions is very important for multi-objective evolutionary algorithms to deal with multi-objective optimization problems (MOPs). In order to achieve the goal, a new orthogonal evolutionary algorithm based on objective space decomposition (OEA/D) is proposed in this paper. To be specific, the objective space of an MOP is firstly decomposed into a set of sub-regions via a set of direction vectors, and OEA/D maintains the diversity of solutions by making each sub-region have a solution to the maximum extent. Also, the quantization orthogonal crossover (QOX) is used to enhance the search ability of OEA/D. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D, NSGAII, NICA and D2MOPSO. Simulation results on six multi-objective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto fronts than other four algorithms.

Suggested Citation

  • Cai Dai & Yuping Wang & Wei Yue, 2015. "A new orthogonal evolutionary algorithm based on decomposition for multi-objective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(10), pages 1686-1698, October.
    • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:10:p:1686-1698
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    Cited by:

    1. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    2. Fei Han & Yu-Wen-Tian Sun & Qing-Hua Ling, 2018. "An Improved Multiobjective Quantum-Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism," Complexity, Hindawi, vol. 2018, pages 1-22, November.
    3. Yue, Wei & Wang, Yuping, 2017. "A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 124-140.
    4. Neungmatcha, Woraya, 2016. "Multi-objective particle swarm optimization for mechanical harvester route planning of sugarcane field operationsAuthor-Name: Sethanan, Kanchana," European Journal of Operational Research, Elsevier, vol. 252(3), pages 969-984.
    5. Chiranjit Changdar & Rajat Kumar Pal & Ghanshaym Singha Mahapatra & Abhinandan Khan, 2020. "A genetic algorithm based approach to solve multi-resource multi-objective knapsack problem for vegetable wholesalers in fuzzy environment," Operational Research, Springer, vol. 20(3), pages 1321-1352, September.
    6. Qi You & Jun Sun & Feng Pan & Vasile Palade & Bilal Ahmad, 2021. "DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    7. Zhang, XuWei & Liu, Hao & Tu, LiangPing & Zhao, Jian, 2020. "An efficient multi-objective optimization algorithm based on level swarm optimizer," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 177(C), pages 588-602.

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