Isolation Forest Backward Particle Swarm Optimization Algorithm and Its Application to Control Problems

Premature convergence is a critical issue of Particle Swarm Optimization (PSO). The weak global search capability causes particles trapped in local minima at early stage of learning process. There is several research dedicate to solve this problem over the decade. This paper proposes a new algorithm combined Isolation Forest and Particle Swarm Optimization called Isolation Forest Backward Particle Swarm Optimization (IFB-PSO). The proposed new learning scheme helps particles escaping from local minima. The particle will jump backward to the targeted position when the particle trapped over specific iterations. The destination is precisely selected by Isolation Forest to endow the backward particle hopeful future. IFB-PSO is evaluated by a classic benchmark suite, cart-pole problem, and mountain car problem. Experimental results show that IFB-PSO gets competitive results on the benchmark suite with different dimensions and two control problems in comparison with 11 well-known optimization algorithms. The behavior of backward particles is also analyzed to inspect the utility and efficiency of the backward process.