Borowska Bożena
Institute of Information Technology, Lodz University of Technology, 93-590 Lodz, Poland.
Entropy (Basel). 2022 Feb 16;24(2):283. doi: 10.3390/e24020283.
Particle swarm optimization (PSO) is a popular method widely used in solving different optimization problems. Unfortunately, in the case of complex multidimensional problems, PSO encounters some troubles associated with the excessive loss of population diversity and exploration ability. This leads to a deterioration in the effectiveness of the method and premature convergence. In order to prevent these inconveniences, in this paper, a learning competitive swarm optimization algorithm (LCSO) based on the particle swarm optimization method and the competition mechanism is proposed. In the first phase of LCSO, the swarm is divided into sub-swarms, each of which can work in parallel. In each sub-swarm, particles participate in the tournament. The participants of the tournament update their knowledge by learning from their competitors. In the second phase, information is exchanged between sub-swarms. The new algorithm was examined on a set of test functions. To evaluate the effectiveness of the proposed LCSO, the test results were compared with those achieved through the competitive swarm optimizer (CSO), comprehensive particle swarm optimizer (CLPSO), PSO, fully informed particle swarm (FIPS), covariance matrix adaptation evolution strategy (CMA-ES) and heterogeneous comprehensive learning particle swarm optimization (HCLPSO). The experimental results indicate that the proposed approach enhances the entropy of the particle swarm and improves the search process. Moreover, the LCSO algorithm is statistically and significantly more efficient than the other tested methods.
粒子群优化算法（PSO）是一种广泛应用于解决各种优化问题的流行方法。不幸的是，在复杂的多维问题中，PSO会遇到一些与种群多样性和探索能力过度丧失相关的问题。这导致该方法的有效性下降和早熟收敛。为了避免这些不便，本文提出了一种基于粒子群优化方法和竞争机制的学习竞争群优化算法（LCSO）。在LCSO的第一阶段，群体被划分为子群体，每个子群体可以并行工作。在每个子群体中，粒子参与锦标赛。锦标赛的参与者通过向竞争对手学习来更新他们的知识。在第二阶段，子群体之间进行信息交换。在一组测试函数上对新算法进行了检验。为了评估所提出的LCSO的有效性，将测试结果与通过竞争群优化器（CSO）、综合粒子群优化器（CLPSO）、PSO、全信息粒子群（FIPS）、协方差矩阵自适应进化策略（CMA-ES）和异构综合学习粒子群优化（HCLPSO）所取得的结果进行了比较。实验结果表明，所提出的方法提高了粒子群的熵并改善了搜索过程。此外，LCSO算法在统计上比其他测试方法更有效。
