LCS & GBML Central

Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems
since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based
memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework
of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator
and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random
immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks
in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic
algorithm is robust and adaptable in dynamic environments.