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A shuffled complex evolution of particle swarm optimization algorithm called SCE-PSO is introduced in this paper. In the SCE-PSO, a population of points is sampled randomly in the feasible space. Then the population is partitioned into several complexes, which is made to evolve based on PSO. At periodic stages in the evolution, the entire population is shuffled and points are reassigned to complexes to ensure information sharing. Both theoretical and numerical studies of the SCE-PCO are presented. Five optimization problems with commonly used functions are utilized for evaluating the performance of the proposed algorithm, and the performance of the proposed algorithm is compared to PSO to demonstrate its efficiency.

A shuffled complex evolution of particle swarm optimization algorithm called SCE-PSO is introduced in this paper. In SCE-PSO, a population of points sampled randomly from the feasible space is partitioned into several complexes, each of which is permitted to evolve independently based on particle swarm optimization (PSO). At periodic stages in the evolution, the entire population is shuffled and points are reassigned to complexes to ensure information sharing. In the simulation part, numerical experiences and case study are performed, and the performance of the proposed algorithm is compared to PSO to demonstrate its efficiency.