Advances in Metaheuristics for Hard Optimization

Many advances have been made recently in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general-purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and various metaheuristics.

The book gathers contributions related to the following topics: theoretical developments in metaheuristics; adaptation of discrete metaheuristics to continuous optimization; performance comparisons of metaheuristics; cooperative methods combining different approaches; parallel and distributed metaheuristics for multiobjective optimization; software implementations; and real-world applications.

This book is suitable for practitioners, researchers and graduate students in disciplines such as optimization, heuristics, operations research, and natural computing.

• Hidden Markov Model
• algorithms
• data mining
• evolutionary algorithm
• genetic algorithms
• global optimization
• heuristics
• learning
• linear optimization
• metaheuristic
• multi-objective optimization
• nonlinear optimization
• operations research
• optimization
• scheduling

• Laboratory LiSSi, University of Paris 12, Créteil, France

  Patrick Siarry

• School of Computer Science, University of Adelaide, Adelaide, Australia

  Zbigniew Michalewicz

Policies and ethics