天赋发掘算法:一种新的元启发式优化算法  

Talent discovery algorithm: a novel meta-heuristic optimization algorithm

作　　者：陈笑通 贾云伟[1,2] 王永霞 张志威 苏起起 CHEN Xiaotong;JIA Yunwei;WANG Yongxia;ZHANG Zhiwei;SU Qiqi(Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control,Tianjin University of Technology,Tianjin 300384,China;National Demonstration Center for Experimental Mechanical and Electrical Engineering Education,Tianjin University of Technology,Tianjin 300384,China;Xinjiang Uygur Autonomous Region Research Institute of Measurement and Testing,Urumqi 830011,China)

机构地区：[1]天津理工大学天津市先进机电系统设计与智能控制重点实验室,天津300384 [2]天津理工大学机电工程国家级实验教学示范中心,天津300384 [3]新疆维吾尔自治区计量测试研究院,新疆乌鲁木齐830011

出　　处：《天津理工大学学报》2022年第3期37-43,共7页Journal of Tianjin University of Technology

基　　金：国家自然科学基金(61873188);天津市自然科学基金(18JCYBJC19300)。

摘　　要：元启发式优化算法解决复杂多峰函数时普遍存在稳定性较低和易于陷入局部最优的情况,针对这一情况,提出一种新颖的、受现代社会环境下孩子天赋发掘启发的元启发式优化算法——天赋发掘算法(talent discoveral algorithm,TDA)。TDA通过模拟社会各群体各司其职又相互合作发掘孩子天赋的过程,实现最优解的寻找。使用可变维多峰基准函数和固定维多峰基准函数对TDA进行测试,结果表明:与正余弦算法(sine cosine algorithm,SCA)、灰狼算法(grey wolf optimizer,GWO)、鲸鱼算法(whale optimization algorithm,WOA)和粒子群算法(particle swarm optimization,PSO)等相比,TDA算法在解决优化问题时具有更强的全局搜索能力和更高的稳定性。When meta-heuristic optimization algorithms solve complex multimodal functions,they generally have low stability and are easy to fall into local optimum. Aiming at this situation,this paper proposes a novel meta-heuristic optimization algorithm,talent discover algorithm(TDA),which is inspired by children’s talent discovery in modern social environment.The TDA realizes the search for the optimal solution by simulating the process of various social groups performing their own duties and cooperating with each other to explore the talents of children. The TDA was tested by variable dimensional multimodal benchmark functions and fixed-dimension multimodal benchmark functions. The results show that the TDA compares with sine cosine algorithm(SCA),grey wolf optimizer(GWO),whale optimization algorithm(WOA)and particle swarm optimization(PSO),the proposed mothed has stronger global search ability and higher stability when solving optimization problems.

关 键 词：元启发式优化算法 复杂多峰函数 天赋发掘算法 稳定性 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]