Optimization

This book offers a comprehensive and accessible exploration of numerical optimization methods, covering both exact approaches (such as linear programming, the simplex method, dynamic programming) and heuristic and metaheuristic approaches (genetic algorithms, particle swarms, ant colonies, simulated annealing). It is aimed at students, engineers and researchers wishing to understand how to formulate, model and efficiently solve optimization problems encountered in a variety of contexts. The book is supported by numerous detailed examples, clear illustrations and MATLAB source codes to facilitate practical application. It compares the advantages, limitations and fields of application of the different methods, and discusses the criteria for choice according to problem characteristics. Finally, it looks ahead to recent developments, notably the integration of artificial intelligence techniques and modern numerical tools in the field of optimization.