A new neighborhood topology for QUAntum particle swarm optimization (QUAPSO)

Swarm Intelligence (SI) is a behavior, used first by Beni and Wang, corresponding to a system working with single and self-organized agents, interacting the ones with each other. This operating concept is implemented in many algorithms. Developed by Kennedy, Eberhart and Shi, Particle Swarm Optimization (PSO) is one of them. Its behavior is based on the movements of birds swarm, and its effectiveness, for looking for the optimal solution of a given problem, is well established. Nevertheless, PSO is known for its weakness in local search. Moreover, the behavior of PSO strongly depends on internal parameters settings. In order to address these problems, we propose a new type of self-adaptive Quantum PSO (QPSO), called QUAntum Particle Swarm Optimization (QUAPSO), based on quantum superposition to set the velocity parameters and hybridized with a Kangaroo Algorithm in order to optimize its efficiency in local search. QUAPSO was compared with five known algorithms from the literature (classical PSO, Kangaroo Algorithm, Simulated Annealing Particle Swarm Optimization, Bat Algorithm and Simulated Annealing Gaussian Bat Algorithm) on a set of 19 test functions. The results show that QUAPSO outperforms the competing algorithms.