Multi-objective opposition-based learning fully informed particle swarm optimizer with favour ranking

Some particle swarm optimization(PSO) algorithms have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. In this paper, we propose a new opposition-based learning fully informed particle swarm optimizer with favour ranking to solve multi-objective optimization problems. Instead of Pareto dominance, favour ranking is used to identify the best individuals in order to guide the search process in the proposed algorithm. Different from other multi-objective PSO, particles in swarm only have position without velocity and the personal best position gets updated using opposition-based learning and favour ranking. Besides, all personal best positions are considered to update particle position. Because of discarding the particle velocity and using full information and favour ranking, the algorithm is the simpler and more effective. The proposed algorithm is applied to some well-known benchmarks. Convergence metric, diversity metric are used to evaluate the performance of the algorithm. The experimental results show that the algorithm is effective on the benchmark functions.