Finding exact solutions to many combinatorial optimization problems in business, engineering, and science remains challenging, despite advances in mathematical programming and computer technology. New application fields, such as computational biology, electronic commerce, and supply chain management, introduce additional challenges that necessitate novel algorithms and optimization techniques. Metaheuristics serve as master procedures that enhance the performance of subordinate heuristics, yielding improved approximate solutions to complex optimization problems compared to simpler algorithms. They offer fast and robust tools that generate high-quality solutions within reasonable computation times. The field of metaheuristics has rapidly evolved, with techniques like simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighborhood search, and ant systems emerging as some of the most effective optimization strategies for real-life problems. Numerous successful applications of metaheuristics are documented in literature, spanning various books, journals, and conference proceedings. International conferences dedicated to the theory, applications, and computational advancements in metaheuristics have seen a growing number of participants from both academia and industry.
M.P. Bastos Bücher
