A Guide to Meta-Heuristic Algorithms for Multi-objective Optimization: Concepts and Approaches

Discovering the best solutions for issues where many, frequently competing objectives must be optimized simultaneously is the difficult task of multi-objective optimization. These approaches have become more common lately because of their capacity to simultaneously optimize many objectives in a range of areas, including finance, engineering, and healthcare. In a variety of disciplines, including engineering, economics, and medical and environmental management, meta-heuristic algorithms have been demonstrated to be successful at resolving challenging, multi-objective optimization issues. To address multi-objective optimization problems, the most popular meta-heuristic approaches, for example, genetic algorithm, particle swarm optimization algorithm, and ant colony optimization—are briefly addressed in this chapter. The main topics of discussion are the technique and applications of meta-heuristic techniques in multi-objective optimization. We additionally examine these algorithms’ advantages and disadvantages in terms of resolving multi-objective optimization issues. In this chapter, a general view of multi-objective optimization ideas is discussed by applying popular meta-heuristic algorithms. Additionally, we investigate the challenges and future directions of multi-objective optimization and its potential impact on society.