Swarm intelligence
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computers in Biology and Medicine
Hi-index | 0.00 |
This paper proposes a two sub-warms particle swarm optimization algorithm (TSPSO) and its iteration equations. The new algorithm assumes that particles are divided into two sub-swarms. The two sub-swarms have different move directions. One sub-swarm moves toward the global best position. Another moves in the opposite direction. Not only its own move experience and the best individual's position of its own sub-swarm, but also the global best position of the whole swarm can affect each particle's move in every iteration. If the fitness of the global best position can't be improved for fifteen successive steps, the particles of the two sub-swarms are exchanged. At the same time, the worst individual of one sub-swarm is replaced with the best individual of another. Then, both TSPSO and PSO are used to resolve ten well-known and widely used test functions' optimization problems. Results show that TSPSO has greater optimization efficiency, better optimization performance and more advantages in many aspects than PSO.