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A Hybrid Search Model for Constrained Optimization

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  • Xiaoli Gao
  • Yangfei Yuan
  • Jie Li
  • Weifeng Gao
  • Shi Cheng

Abstract

This paper proposes a hybrid model based on decomposition for constrained optimization problems. Firstly, a constrained optimization problem is transformed into a biobjective optimization problem. Then, the biobjective optimization problem is divided into a set of subproblems, and different subproblems are assigned to different Fitness functions by the direction vectors. Different from decomposition-based multiobjective optimization algorithms in which each subproblem is optimized by using the information of its neighboring subproblems, the neighbors of each subproblem are deFined based on corresponding direction vector only in the method. By combining three main components, namely, the local search model, the global search model, and the direction vector adjusting strategy, the population can gradually move toward the global optimal solution. Experiments on two sets of test problems and Five real-world engineering design problems have shown that the proposed method performs better than or is competitive with other compared methods.

Suggested Citation

  • Xiaoli Gao & Yangfei Yuan & Jie Li & Weifeng Gao & Shi Cheng, 2022. "A Hybrid Search Model for Constrained Optimization," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-15, September.
  • Handle: RePEc:hin:jnddns:1190174
    DOI: 10.1155/2022/1190174
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    Cited by:

    1. Yuhan Bai & Jiajia Zhao & Hangrui Shen & Xinyao Li & Bo Wen, 2024. "The Evolution of Forest Landscape Connectivity and Ecological Network Construction: A Case Study of Zhejiang’s Ecological Corridors," Sustainability, MDPI, vol. 16(14), pages 1-24, July.

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