面向全局和工程优化问题的混合进化JAYA算法  

Hybrid evolutionary JAYA algorithm for global and engineering optimization problems

作　　者：刘景森[1,2] 杨杰 李煜[3] LIU Jing-sen;YANG Jie;LI Yu(Institute of Intelligent Networks System,Henan University,Kaifeng 475004,China;College of Software,Henan University,Kaifeng 475004,China;Institute of Management Science and Engineering,Henan University,Kaifeng 475004,China)

机构地区：[1]河南大学智能网络系统研究所,开封475004 [2]河南大学软件学院,开封475004 [3]河南大学管理科学与工程研究所,开封475004

出　　处：《工程科学学报》2023年第3期431-445,共15页Chinese Journal of Engineering

基　　金：河南省重点研发与推广专项资助项目(182102310886,222102210065);国家自然科学基金资助项目(71601071)。

摘　　要：为了更好求解复杂函数优化和工程约束优化问题,进一步增强JAYA算法的寻优能力,提出一种面向全局优化的混合进化JAYA算法.首先在计算当前最优和最差个体时引入反向学习机制,提高最优和最差个体跳离局部极值区域的可能性;然后在个体位置更新中引入并融合正弦余弦算子和差分扰动机制,不仅增加了种群的多样性,而且较好平衡与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;最后在算法结构上采用奇偶不同的混合进化策略,有效利用不同演化机制的优势结果,进一步提升了算法的收敛性和精度.之后给出了算法流程伪代码,理论分析证明了改进算法的时间复杂度与基本JAYA相同,而通过6种代表性算法在包含和组合了30个基准函数的CEC2017测试套件上进行的多维度函数极值优化测试,以及对拉伸弹簧、波纹舱壁、管柱设计、钢筋混凝土梁、焊接梁和汽车侧面碰撞6个具有挑战性的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、收敛性能和求解稳定性均有显著提升,在求解CEC复杂函数和工程约束优化问题上有着明显优势.A swarm intelligence optimization algorithm is an effective method to rapidly solve large-scale complex optimization problems.The JAYA algorithm is a new swarm intelligence evolutionary optimization algorithm,which was proposed in 2016.Compared with other active evolutionary algorithms,the JAYA algorithm has several advantages,such as a clear mechanism,concise structure,and ease of implementation.Further,it has guiding characteristics,obtains the best solution,and avoids the worst solution.The JAYA algorithm has an excellent optimization effect on many problems,and it is one of the most influential algorithms in the field of swarm intelligence.However,when dealing with the CEC test suite,which contains and combines shifted,rotation,hybrid,combination,and other composite characteristics,and the complex engineering constrained optimization problems with considerable difficulty and challenges,the JAYA algorithm has some flaws,that is,it easily falls into the local extremum,its optimization accuracy is sometimes low,and its solution is unstable.To better solve complex function optimization and engineering constrained optimization problems and further enhance the optimization capability of the JAYA algorithm,a global optimization-oriented hybrid evolutionary JAYA algorithm is proposed.First,opposition-based learning is introduced to calculate the current best and worst individuals,which improves the possibility of the best and worst individuals jumping out of the local extremum region.Second,the sine–cosine operator and differential disturbance mechanism are introduced and integrated into individual position updating,which not only improves the diversity of the population but also better balances and meets the different requirements of the algorithm for exploration and mining in different iteration periods.Finally,in the algorithm structure,the hybrid evolution strategy with different parity states is adopted and the advantages of different evolution mechanisms are effectively used,which further improves the converg

关 键 词：JAYA算法 CEC2017 正弦余弦算子 奇偶进化策略 工程设计优化问题 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]