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求解高维优化问题的ITCSO算法

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为提高竞争群优化(competitive swarm optimization，CSO)算法求解高维优化问题的寻优效率，提出一种改进的3重竞争群优化(improved triple competitive swarm optimization，ITCSO)算法。首先，采用3重竞争机制提高算法的寻优效率，同时，获胜粒子较好的收敛基础可以提高失败粒子的个体认知，明确粒子更新方向以提高粒子探索能力;然后，提出优败粒子向获胜子群学习的策略，增强算法的社会认知能力，减少算法评估次数，从而提高算法全局搜索能力;最后，提出获胜子群自竞争和劣败粒子基于获胜者变异的操作，增强粒子局部开发能力，避免算法陷入局部最优。为验证所提出算法的可行性，通过计算系统状态转移矩阵特征值和使用极限分析方法，给出稳定性和收敛性理论证明。采用几种基准测试函数验证所提出算法求解高维问题时的性能，并与其他算法进行对比。实验结果表明，ITCSO算法不仅有较高的寻优效率，且全局搜索和局部开发能力突出，更适用于高维问题的求解。
ITCSO algorithm for solving high dimensional optimization problem
In order to improve the optimization efficiency of a competitive swarm optimization(CSO)algorithm,an improved triple competitive swarm optimization(ITCSO)algorithm is proposed for solving high-dimensional optimization problems.Firstly,a triple competition mechanism is used to improve the optimization efficiency of the algorithm.Simultaneously,the better convergence basis of the winners can improve the cognitive ability of the losers,and can guide the adaption direction of particles to improve the exploration ability.Secondly,the strategy that the losers with superiority fitness can learn from the winning subgroup is proposed,which can enhance the social cognition ability and reduce the number of evaluations,and can greatly improve the global search ability.Finally,the winning subgroup self-competition and the variation of losers with inferior fitness based on winners is proposed to enhance the local explore ability,which can avoid the algorithm falling into local optimum.In order to demonstrate the feasibility of the ITCSO algorithm,the stability and the convergence are proved by calculating the eigenvalues of the state transition matrix and using the limit analysis method.Several benchmark test functions are adopted to verify the performance of the proposed ITCSO.The experimental results show that,compared with other algorithms,the ITCSO not only has high optimization efficiency,but also has outstanding global search and local explore ability,which is more suitable for solving the high-dimensional problems.

particle swarm optimizationcompetitive swarm optimizationhigh dimensional optimizationtriple competition mechanismlocal exploreconvergence analysis

张伟、魏万峰、黄卫民

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河南理工大学电气工程与自动化学院,河南焦作 454003

粒子群优化算法 竞争群优化算法 高维优化 3重竞争机制 局部开发 收敛性分析

国家自然科学基金项目河南省科技攻关项目河南省高校科技创新团队项目

6170314522210221021320IRTSTHN019

2024

10.13195/j.kzyjc.2022.0327

控制与决策
东北大学

控制与决策

CSTPCD北大核心
影响因子：1.227
ISSN：1001-0920
年,卷(期)：2024.39(2)
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