A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments
This addresses the challenge of tracking changing optima in dynamic environments for optimization algorithm users, though it appears incremental as it combines existing techniques like PSO and clonal selection.
The paper tackled dynamic optimization problems where optima change over time by proposing a multi-swarm cellular PSO based on a clonal selection algorithm (CPSOC), which outperformed popular methods on the Moving Peaks Benchmark.
Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make a sub-swarm, are evolved by the particle swarm optimization and clonal selection algorithm. Experimental results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its popular methods.