Paper Contents
Abstract
Optimization is a critical field of study aimed at finding the most effective solutions from a range of alternatives to achieve specific objectives. While single-objective optimization problems focus on optimizing a single criterion, real-world applications often involve multiple conflicting objectives, necessitating multi-objective optimization problems (MOPs). Traditional methods, such as gradient-based techniques, excel in single-objective scenarios but struggle with the complexity of MOPs. Genetic Algorithms (GAs) offer a robust alternative, inspired by evolutionary processes, capable of handling multiple objectives simultaneously by exploring a diverse set of solutions and approximating the Pareto front.This research paper evaluates the efficiency of Genetic Algorithms in solving MOPs compared to other optimization techniques. The paper provides a comprehensive analysis of various optimization methods, with a particular focus on Multi-Objective Evolutionary Algorithms (MOEAs). Among these, the Strength Pareto Evolutionary Algorithm 2 (SPEA2) is highlighted for its effective balance of convergence and diversity. Through detailed case studies and benchmarks, including a real-world manufacturing process optimization example, the paper assesses the performance of GAs and SPEA2 in terms of convergence speed, solution diversity, and computational efficiency.The findings reveal that while GAs and MOEAs like SPEA2 are highly effective in navigating complex multi-objective landscapes, they also face challenges related to computational cost and convergence speed. The paper concludes with insights into the strengths and limitations of these algorithms and suggests potential improvements for broader applicability in solving diverse multi-objective optimization problems.Keywords: Optimization, Genetic Algorithms (GAs), Pareto Front, Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Evolutionary Algorithms (MOEAs).
Copyright
Copyright © 2024 Ayushi Gupta . This is an open access article distributed under the Creative Commons Attribution License.
