This book introduces a novel hybrid algorithm designed to tackle complex multiobjective optimization problems in engineering. The authors present a fusion of Genetic Algorithms and Particle Swarm Optimization, initially developed for single-objective problems, and extend it to handle multiple objectives, aiming to generate a set of non-dominated solutions. A Local Search Technique is incorporated to refine these solutions further, enhancing the proximity to optimal outcomes. The efficacy of this proposed approach is validated through its application to both single and multiobjective optimization challenges, including a critical engineering problem in electric power systems: multiobjective reactive power compensation.

The book presents a technical approach to optimization, likely appealing to researchers and practitioners in computer science and engineering. The detailed methodology, combining established AI techniques like Genetic Algorithms and Particle Swarm Optimization with a novel hybrid structure and local search, suggests a focus on algorithmic development and practical application. The inclusion of a specific engineering case study in electric power systems indicates an effort to demonstrate the real-world relevance and effectiveness of the proposed methods. The work appears to be a specialized contribution aimed at advancing the state-of-the-art in multiobjective optimization for complex engineering challenges.