多策略融合改进的自适应被囊群算法  

Multi-strategy fusion improved adaptive tunicate swarm algorithm

作　　者：柴岩[1] 李广友 任生 许兆楠 Chai Yan;Li Guangyou;Ren Sheng;Xu Zhaonan(College of Science,Liaoning Technical University,Fuxin Liaoning 123000,China)

机构地区：[1]辽宁工程技术大学理学院,辽宁阜新123000

出　　处：《计算机应用研究》2023年第9期2694-2703,2712,共11页Application Research of Computers

基　　金：教育部规划基金青年项目(21YJCZH204);辽宁省自然科学基金资助项目(2020-MS-301);辽宁省教育厅项目(LJKMZ20220694)。

摘　　要：针对被囊群算法全局搜索不充分和易陷入局部极值等问题,提出一种多策略融合改进的自适应被囊群算法(MITSA)。首先,在种群初始化中引入佳点集理论提升种群多样性;其次,提出一种多精英协同引导机制优化被囊个体位置信息,增大对未知搜索区域的勘探可能性以增强算法全局探索能力;然后将自适应权重因子引入群体行为阶段,动态平衡算法的全局与局部搜索性能;接着,为增强算法的抗停滞能力,采用依概率小波变异策略实现个体动态微调,同时利用贪婪原则保留优异信息助推种群向食物源靠近;最后基于Markov链理论对改进算法的全局收敛性进行分析论证。通过对基准测试函数和CEC2014复杂函数进行数值仿真,实验结果与Wilcoxon秩和检验结果综合验证了MITSA具有优越的收敛精度、稳健的鲁棒性和高维可拓展性。In order to solve the problems of inadequate global search and easy to fall into local extremum,this paper proposed a multi-strategy fusion improved adaptive tunicate swarm algorithm(MITSA).Firstly,this paper used the best point set strategy to improve population diversity during initialization.Secondly,it offered a multi-elite cooperative guidance mechanism to optimize the tunicates’location information,and enhance the global exploration ability of the algorithm by increasing the exploration possibility of unknown search area.Then,the algorithm achieved dynamic global and local search balance by introducing adaptive weight factors into the group behavior stage.Meanwhile,probabilistic wavelet variation strategy promoted individual dynamic fine-tuning to enhance the anti-stagnation ability of the algorithm,while using the greedy principle to retain excellent information to help the population to the food source.Finally,this paper proved the global convergence of the improved algorithm based on Markov chain theory.Through the numerical simulation of the benchmark test function and CEC2014 complex function,the experimental results and Wilcoxon rank sum test results comprehensively verify MITSA’s excellent convergence accuracy,robust robustness and high-dimensional scalability.

关 键 词：被囊群算法 佳点集 多精英协同引导 自适应权重 小波变异 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]