Parallel Binary Particle Swarm Optimization Algorithm Based on Immunity for Solving Knapsack Problem

To realize optimization problems with discrete binary variables, a modified hybrid binary particle swarm optimization(MHBPSO) was proposed. To simplify the structure of MHBPSO algorithm, the theories of immunity in biology and parallel computation were introduced. The catfish effect and the operation of crossover and mutation were also embedded in order to avoid the local convergence and stagnation and maintain the diversity of swarm's searching positions during the later period of MHBPSO algorithm. Simulation performance of different mature discrete optimization algorithms were compared by solving classical 0-1 knapsacks problems. The simulation results show that MHBPSO has a simple structure, high convergence speed and superior global optimization capability, which is an efficient method for discrete optimization problems.