A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions
A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions
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DOI：
10.1109/access.2022.3222530
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Haiyang Xiao;Jianzhong Guo;Binghua Shi;Yi Di;Chao Pan;Ke Yan;Yuji Sato
中科院分区：
文献类型：
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作者：
Haiyang Xiao;Jianzhong Guo;Binghua Shi;Yi Di;Chao Pan;Ke Yan;Yuji Sato
陷入局部极小是非线性优化问题中的一个重要问题，它阻碍了进化算法寻找全局最优解。通常，为了提高优化精度，进化算法围绕最佳个体进行搜索。然而，过度使用来自单个个体的信息会导致种群的多样性迅速丧失，从而降低搜索能力。为了克服这个问题，一个孪生记忆的骨干粒子群优化（TMBPSO）算法在这项工作中。TMBPSO包含一个缠绕记忆存储机制（TMSM）和一个多记忆检索策略（MMRS）。TMSM使额外的存储空间，以扩大粒子群的搜索能力和MMRS增强粒子群的局部极小值逃逸能力。通过TMSM和MMRS的协同工作，使粒子群具有自校正能力。为了验证TMBPSO的搜索能力，在实验中选择了CEC2017基准函数和五种最先进的基于种群的优化算法。最后，实验结果表明，TMBPSO可以获得高精度的非线性函数的结果。
Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of self-rectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.