A Novel Binary Quantum-Behaved Particle Swarm Optimization Algorithm

Abstract

To keep the balance between the global search and local search, a novel binary quantum-behaved particle swarm optimization algorithm with comprehensive learning and cooperative approach (CCBQPSO) is presented. In the proposed algorithm, all the particles' personal best position can participate in updating the local attractor firstly. Then all the particles' previous personal best position and swarm's global best position are performed in each dimension of the solution vector. Five test functions are used to test the performance of CCBQPSO. The results of experiment show that the proposed technique can increase diversity of swarm and converge more rapidly than other binary algorithms.