Innovative Metaheuristics

This book explores advanced metaheuristic optimization techniques developed to tackle increasingly complex real-world problems that traditional methods struggle to solve. It introduces improved versions of the Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA), addressing issues such as slow convergence and local optima stagnation. The first part covers the theoretical foundations and enhancements made to these algorithms, including the integration of Lévy flight and logarithmic spiral functions in SSA (ISSA) and an arithmetic crossover mechanism in GOA (IGOA). The effectiveness of these algorithms is demonstrated through their application to engineering problems, particularly multimodal challenges. The second part evaluates the capabilities of the proposed methods in real-world multi-objective optimization problems, such as performance index minimization and photovoltaic parameter estimation, providing comprehensive experimental results that highlight their superior efficiency and applicability.