Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization
Announcing the publication of “Meta-heuristic and Evolutionary Algorithms for Engineering Optimization”, co-authored by UCSB Department of Geography faculty Hugo A. Loaiciga.
This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.
- Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization;
- Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner;
- Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms;
- Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering;
- Relates optimization algorithms to engineering problems employing a unifying approach.