A hybrid constrained optimization approach coupling PSO and adaptive constraint-handling technique

In this paper, we present a novel hybrid approach combining particle swarm optimization (PSO) and adaptive constraint-handling technique (ACT) for solving constrained numerical and engineering optimization problems. The proposed hybrid approach simultaneously adopts particle swarm optimizer and hybrid mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique includes three main situations. In each situation, a constraint-handling mechanism is designed based on current population state. Our algorithm is validated using 15 well-known constrained numerical and engineering optimization problems reported in the literature. The experimental results demonstrate that the proposed method shows better performance in comparison to the state-of-the-art algorithms. Key-Words: Constrained optimization problem; Particle swarm optimization; Adaptive constraint-handling technique; Engineering optimization; mutation