Multi Objective Migrating Birds and Particle Swarm Optimization Algorithms

Multiobjective optimization problems (MOPs) involve optimizing two or more conflicting objectives, often subject to several constraints. Solving such problems efficiently requires algorithms that can find Pareto-optimal solutions, where no solution can be improved in any objective without degrading another. Migrating Birds Optimization (MBO) is a nature- inspired algorithm that mimics the migration behavior of birds. This paper introduces an enhanced version of MBO, tailored for solving MOPs, and compares its performance with Particle Swarm Optimization (PSO). The proposed MBO algorithm, specifically designed for multiobjective problems, incorporates constraint handling mechanisms and the concept of Pareto dominance to find Pareto-optimal solutions. The effectiveness of the algorithm is demonstrated on a multiobjective problem with three constraints, with comparisons to PSO using Python and graphical results.