A Multi-Swarm Cellular PSO based on Clonal Selection Algorithm in Dynamic Environments

TL;DR

This paper introduces CPSOC, a novel multi-swarm cellular PSO algorithm based on clonal selection, designed to effectively track optima in dynamic environments by partitioning the search space and evolving sub-swarms.

Contribution

The paper proposes a new multi-swarm cellular PSO algorithm integrating clonal selection for dynamic optimization, enhancing tracking ability over existing methods.

Findings

CPSOC outperforms popular methods on Moving Peaks Benchmark.

The cellular automaton partitioning improves search efficiency.

Clonal selection enhances adaptability in dynamic environments.

Abstract

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.