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.

中文翻译：

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基于物种守恒的粒子群优化算法，具有局部搜索的动态优化问题。

在优化动态环境中的问题时，算法不仅需要找到特定环境中的全局最优解，而且还需要在动态环境中不断跟踪移动的最优解。为了满足这一要求，提出了一种基于物种保护的粒子群优化算法（PSO），并结合了空间邻域最佳搜索技术。该算法采用物种保护技术来保存发现的分布在搜索空间中的最优值，这些保存的最优值要么转移到新种群中，要么在随后的进化过程中的一定距离内被更好的个体替代。种群中的粒子被其历史最佳和附近的最佳解决方案所吸引，它们基于基于欧几里德距离而不是基于指数的解决方案。根据移动峰值基准进行了一项实验研究，与广泛应用于动态优化问题的几种最新算法相比，该算法的性能得到了验证。实验结果表明了该算法在动态环境下跟踪运动最优的有效性和有效性。