Hybrid-particle Interaction Particle Swarm Optimization Algorithm

To overcome the searching shortages of the existing particle swarm optimization algorithms only considered a single kind of attraction and repulsion rules, different attraction and repulsion rules should be considered in different searching stages, later-stage attraction-enhanced hybrid attraction and repulsion particle swarm optimization algorithm(LAPSO algorithm) is proposed: At the early stage, the diversity of particles are maintained by the rules of attraction and repulsion in artificial physics, to improve the global searching ability; When the particles move to the global optimal solution area, enhanced the effect of attraction and reduced the effect of repulsion, using the attractions of other particles with better fitness values and the global optimal solution particle to improve the local searching ability. In order to further improve the optimal performance of LAPSO algorithm, hybrid-particle interaction particle swarm optimization algorithm(HIPSO algorithm) is proposed by combining LAPSO algorithm and hybrid fully connected-ring topology. The test results of six Benchmark functions show that the proposed LAPSO algorithm and HIPSO algorithm have better population diversity, better optimization precision, convergence rate and optimal solution searching ability than the existing extended-particle swarm optimization algorithm, micro-particle swarm optimization algorithm and median-oriented particle swarm optimization algorithm. The optimal solution searching ability of HIPSO algorithm is verified by the discrete optimization example of flexible flow-shop scheduling in the reference [7] and continuous optimization example of ultrasonic vibration process parameters in the reference [20].