Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired algorithm. However, it suffers search stagnation from being trapped into sub-optimal solution in an problem. This paper proposes novel hybrid (SDPSO) to improve its performance on local searches. merges two strategies, the static exploitation (SE, velocity updating strategy considering inertia-free velocity), and direction (DS) of Rosenbrock method, original PSO. With this hybrid, one hand, extensive exploration still maintained by PSO; other SE responsible for locating small region, then DS further intensifies search. SDPSO was implemented tested unconstrained benchmark problems (CEC2014) some constrained engineering design problems. compared with that algorithms, results show has competitive performance.