  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.
