Evolutionary Algorithms for Complex Problem Solving: Utilizing Evolutionary Computations to Address Complex, Multi-Objective Optimization Problems

Abstract

The domain of complex problem-solving often requires innovative approaches due to the inherent complications in multi-objective optimization. Evolutionary algorithms (EAs) have emerged as a robust methodology to tackle these challenges. This review article delineates the fundamental principles of evolutionary algorithms and their application in addressing complex, multi-objective optimization problems. It highlights the versatility of EAs in adapting to evolving problem landscapes through mechanisms inspired by natural evolution. By analyzing contemporary research, this article elucidates the efficacy of various evolutionary strategies, such as genetic algorithms, differential evolution, and particle swarm optimization, in handling diverse and multi-faceted optimization tasks. The discussion extends to the integration of hybrid models and the combination of EAs with other computational techniques, presenting a comprehensive outlook on their role in current scientific inquiries. Benefits, limitations, and future trajectories in the utilization of evolutionary computations are also examined, providing insight into ongoing advancements and prospective developments in the field.