College of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.
School of Computer and Information Science, Hubei Engineer University, Xiaogan 432000, China.
Comput Intell Neurosci. 2020 Aug 1;2020:2815802. doi: 10.1155/2020/2815802. eCollection 2020.
In the optimization of problems in dynamic environments, algorithms need to not only find the global optimal solutions in a specific environment but also to continuously track the moving optimal solutions over dynamic environments. To address this requirement, a species conservation-based particle swarm optimization (PSO), combined with a spatial neighbourhood best searching technique, is proposed. This algorithm employs a species conservation technique to save the found optima distributed in the search space, and these saved optima either transferred into the new population or replaced by the better individual within a certain distance in the subsequent evolution. The particles in the population are attracted by its history best and the optimal solution nearby based on the Euclidean distance other than the index-based. An experimental study is conducted based on the moving peaks benchmark to verify the performance of the proposed algorithm in comparison with several state-of-the-art algorithms widely used in dynamic optimization problems. The experimental results show the effectiveness and efficiency of the proposed algorithm for tracking the moving optima in dynamic environments.
在动态环境中的问题优化中，算法不仅需要在特定环境中找到全局最优解，还需要在动态环境中不断跟踪移动最优解。为了解决这个需求，提出了一种基于物种保护的粒子群优化（PSO）算法，结合了空间邻域最佳搜索技术。该算法采用了物种保护技术来保存分布在搜索空间中的已发现最优解，这些保存的最优解在后续的进化中要么转移到新的种群中，要么被一定距离内的更好个体所替代。种群中的粒子基于欧几里得距离而不是基于索引的方式被其历史最佳和附近的最优解所吸引。基于移动峰值基准进行了一项实验研究，以验证所提出算法在与广泛用于动态优化问题的几种最先进算法的比较中的性能。实验结果表明，该算法在动态环境中跟踪移动最优解的有效性和效率。
