Fitness proportional hypermutations within memory-based colonization in particle swarm optimization

In this paper, fitness proportional hypermutations inspired from studies on artificial immune systems are used to improve the performance of a memory-based colonization scheme implemented in PSO. For each particle, externally implemented global- (shared) and particle-based (local) memories are considered for the generation of a colony of velocity and position vectors. In this respect, a set of velocities is computed for each particle using each of the personal best positions within its local memory and each of the global positions from the shared memory. This way, a colony of new positions is obtained for each particle and the one with the best fitness is selected and put within the new swarm. Global and local memories are also updated using the solutions within each colony. Experimental evaluations demonstrated that the proposed strategy outperformed the conventional and other known memory-based PSO algorithms for all problem instances within the CEC2005 test suit.