Solving Multi-Objective Problems Using Bird Swarm Algorithm

Abstract

This paper introduces an effective method by combining the multi-objective technique with the bird swarm algorithm (BSA) to obtain a new method called MBSA. The MBSA obtains some of the different non-dominated techniques that maintain variety amongst the optimal solutions. To verify and evaluate the effectiveness of the MBSA, collections of constrained, unconstrained, and engineering problems are measured. These problems have various Pareto front (PF) properties, including non-convex, convex, and discrete PFs. The results show that the MBSA has a good ability to obtain both a better solution spread and better convergence near the true PF. Furthermore, the quantitative and qualitative results indicate that the MBSA provides high convergence and good results in all experiments and with real-world problems against well-known algorithms in the literature.

Keywords

• Bird swarm algorithm
• multi-objective optimization
• constrained optimization problems
• unconstrained optimization problems
• engineering optimization problems
• Pareto front