Authors : Dr. Mitat Uysal; Dr. Aynur Uysal

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/4n6v87p7

Scribd : https://tinyurl.com/372ye4r4

DOI : https://doi.org/10.38124/ijisrt/25mar689

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Abstract : 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.

Keywords : Multiobjective Optimization, Migrating Birds Optimization, Particle Swarm Optimization, Pareto-Optimal Solutions, Constraint Handling, Nature-İnspired Algorithms.

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