"At the heart of the optimization domain are mathematical modelling of the problem and the solution methodologies. In recent times, the problems are becoming larger, with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to Artificial Intelligence (AI) based nature-inspired solution methodologies or algorithms. The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications, as well as newly devised metaheuristic algorithms. The book will be a valuable reference to researchers from industry and academia, as well as Masters and PhD students around the globe working in the metaheuristics and applications domain"--
This book provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications, as well as newly devised metaheuristic algorithms.
At the heart of the optimization domain are mathematical modelling of the problem and the solution methodologies. In recent times, the problems are becoming larger, with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to Artificial Intelligence (AI) based nature-inspired solution methodologies or algorithms.
The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications, as well as newly devised metaheuristic algorithms.
The book will be a valuable reference to researchers from industry and academia, as well as Masters and PhD students around the globe working in the metaheuristics and applications domain.
