Wu Husheng, Xiao Renbin
School of Equipment Management and Support, Armed Police Force Engineering University, Xi'an 710086, China.
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Research (Wash D C). 2020 Feb 18;2020:1762107. doi: 10.34133/2020/1762107. eCollection 2020.
Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.
优化问题，尤其是在动态环境中的优化问题，是一个热门的研究领域，在过去几十年中受到了显著关注。从动态优化文献中可以清楚地看出，尽管大多数现实生活中的问题是组合问题，但大部分努力都集中在连续动态优化问题上。此外，许多在静态组合优化问题中被证明成功的算法在动态环境中通常表现平平。在本研究中，基于二进制狼群算法（BWPA），结合灵活的种群更新策略，提出了一种灵活的二进制狼群算法（FWPA）。然后，FWPA被用于解决一组静态多维背包基准问题和几个动态多维背包问题，这些问题有许多实际应用。据我们所知，本文是关于狼群算法在动态组合问题上性能的首次研究。通过将两种最先进的算法与基本的BWPA进行比较，仿真实验结果表明，FWPA可被视为一种用于动态优化问题的可行且具有竞争力的算法。
