改进JAYA算法求解工程设计优化问题

刘景森; 杨杰; 李煜

doi:10.12263/DZXB.20211446

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改进JAYA算法求解工程设计优化问题

学术论文 | 更新时间：2025-12-08

- 改进JAYA算法求解工程设计优化问题
- Solving Engineering Optimization Design Problems Based on Improved JAYA Algorithm
- 电子学报 2023年51卷第9期页码：2469-2480
- 作者机构：
  
  1.河南大学智能网络系统研究所，河南开封 475004
  2.河南大学软件学院，河南开封 475004
  3.河南大学管理科学与工程研究所，河南开封 475004
- 作者简介：
  
  [ "刘景森男，1968 年生，河南开封人，博士，教授，研究方向为智能算法、优化控制和网络安全等．" ]
  杨杰男，1993年生，山西忻州人，硕士研究生，研究方向为智能算法． E-mail: yjiehenu@163.com
- 基金信息：
  
  河南省重点研发与推广专项(222102210065);国家自然科学基金(71601071)
- DOI：10.12263/DZXB.20211446
  中图分类号： TP301.6
- 收稿：2021-10-22，
  
  修回：2022-01-02，
  
  纸质出版：2023-09-25
- 稿件说明：
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刘景森,杨杰,李煜.改进JAYA算法求解工程设计优化问题[J].电子学报,2023,51(09):2469-2480.

LIU Jing-sen,YANG Jie,LI Yu.Solving Engineering Optimization Design Problems Based on Improved JAYA Algorithm[J].ACTA ELECTRONICA SINICA,2023,51(09):2469-2480.
刘景森,杨杰,李煜.改进JAYA算法求解工程设计优化问题[J].电子学报,2023,51(09):2469-2480. DOI： 10.12263/DZXB.20211446.

LIU Jing-sen,YANG Jie,LI Yu.Solving Engineering Optimization Design Problems Based on Improved JAYA Algorithm[J].ACTA ELECTRONICA SINICA,2023,51(09):2469-2480. DOI： 10.12263/DZXB.20211446.

摘要

为了更好求解工程设计约束优化问题，进一步提升JAYA算法的全局寻优和应用能力，提出一种基于多角色差异进化策略的改进JAYA算法.首先引入余弦相似度策略，通过旋转变换算子和非均匀变异算子对与最优个体余弦相似度较高的个体位置进行处理，不仅加快了算法的收敛速度，而且丰富了种群的多样性；然后在个体位置更新中采用多角色策略，并引入共生策略和柯西变异机制，有效平衡和较好满足了算法在不同迭代时期对探索和挖掘能力的不同需求，进而改善了算法的优化性能；最后引入小孔成像反向学习策略，则扩大了算法的搜索范围，进一步提高了算法的收敛性和精度.通过对10个复杂标准测试函数进行的多维度、多算法函数极值优化，以及对5个CEC2020中描述的更具挑战性的复杂工程设计问题的优化求解，都清楚地表明改进后算法的寻优精度、收敛性能、求解稳定性及对不同问题的适用性和有效性均有显著提升，在求解工程设计优化问题上有较为明显的优势.

Abstract

In order to better solve the engineering design constrained optimization problem and further improve the global optimization and application ability of the JAYA algorithm

the JAYA algorithm based on multi role differential evolution strategy is proposed. Firstly

the cosine similarity strategy is introduced

the individual positions with high cosine similarity to the optimal individual are processed by rotation change operator and non-uniform mutation operator

which not only accelerates the convergence speed of the algorithm

but also enriches the diversity of the population; Then the multi role strategy is introduced to the individual location updating

and the symbiosis strategy and cauchy mutation mechanism are introduced to effectively balance and better meet the different needs of the algorithm for exploration and mining ability in different iterative periods

which improves the optimization performance of the algorithm; Finally

the pinhole-imaging opposition-based learning strategy is introduced

which expands the search range of the algorithm and further improves the convergence and accuracy of the algorithm. Through the simulation experiment of function extremum optimization of the multi algorithms on multiple dimensions of the 10 complex benchmark test functions and the optimization of 5 more challenging complex engineering design problems described in CEC2020

the test results clearly show that the proposed algorithm has significantly better optimization accuracy

convergence performance

solution stability

applicability and effectiveness to different problems

and it has obvious advantages in solving engineering design optimization problems.

关键词

Keywords

references

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