Advanced Metaheuristics: Novel Approaches for Complex Problem Solving

This book examines a series of strategies designed to enhance metaheuristic algorithms, focusing on critical aspects such as initialization methods, the incorporation of Evolutionary Game Theory to develop novel search mechanisms, and the application of learning concepts to refine evolutionary operators. Furthermore, it emphasizes the significance of diversity and opposition in preventing premature convergence and improving algorithmic efficiency. These strategies collectively contribute to the development of more adaptive and robust optimization techniques. The book was designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in Science, Electrical Engineering, or Computational Mathematics. Furthermore, engineering practitioners unfamiliar with metaheuristic computations will find value in the application of these techniques to address complex real-world engineering problems, extending beyond theoretical constructs.

Optimization.- Metaheuristic Algorithms.- Population initialization for metaheuristic algorithm based on the Gibbs sampling methodology.- Metaheuristic optimization with dynamic strategy adaptation.- Harnessing Locust Swarm Dynamics for Optimization Algorithms.- Diversity-Opposition hybridization of the Cheetah Optimizer for global optimization.