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Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application: Power Allocation Optimization of Mobile Base Stations

Author

Listed:
  • Yu Bo

    (Zhijing Mining Group Co. Ltd., China)

  • Fahui Gu

    (Guangdong Polytechnic, China)

Abstract

In the multi-objective optimization algorithm, the parameter strategy has a huge impact on the performance of the algorithm, and it is difficult to set a set of parameters with excellent distribution and convergence performance in the actual optimization process. Based on the MOEA/D algorithm framework, this paper construct an improved dual-population co-evolution MOEA/D algorithm by adopt the idea of dual-population co-evolution. The simulation test of the benchmark functions shows that the proposed dual-population co-evolution MOEA/D algorithm have significant improvements in IGD and HV indicators compare with three other comparison algorithms. Finally, the application of the LTE base station power allocation model also verifies the effectiveness of the proposed algorithm.

Suggested Citation

  • Yu Bo & Fahui Gu, 2022. "Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application: Power Allocation Optimization of Mobile Base Stations," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 16(1), pages 1-21, January.
    • Handle: RePEc:igg:jcini0:v:16:y:2022:i:1:p:1-21
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    References listed on IDEAS

    as
    1. Rongrong Li & Linrun Qiu & Dongbo Zhang, 2019. "Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 13(2), pages 18-29, April.
    2. Fahui Gu & Wenxiang Wang & Luyan Lai, 2019. "Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 13(2), pages 1-17, April.
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