Metaheuristics has grown and continues to grow steadily. Seen both from the technical point of view and from the application-oriented side, these optimization tools have established their value in a remarkable story of success. Researchers have demonstrated the ability of these methods to solve hard combinatorial problems of practical sizes within reasonable computational time.
Highlighted in METAHEURISTICS: Progress in Complex Systems Optimization are the recent developments made in the area of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms. In addition, a series of tutorials on developing areas in Metaheuristics are presented in the volume. Giving these tutorials are some of the top researchers in Metaheuristics: Edmund Burke, Reuven Rubinstein, Eric Taillard, Gilles Pesant, Pierre Hansen, and Stefan Voß.
Applications addressed are anticipated to include production planning, machine and project scheduling, the traveling salesman and vehicle routing, packing, knapsack and location problems with layout design, portfolio selection, network-design, health care, energy and environmental planning, data mining, pattern classification and biotechnology, among others.
The aim of this book is to provide several different kinds of information: a delineation of general Metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.
The aim of METAHEURISTICS: Progress in Complex Systems Optimization is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.
Highlighted are recent developments in the areas of Simulated Annealing, Path Relinking, Scatter Search, Tabu Search, Variable Neighborhood Search, Hyper-heuristics, Constraint Programming, Iterated Local Search, GRASP, bio-inspired algorithms like Genetic Algorithms, Memetic Algorithms, Ant Colony Optimization or Swarm Intelligence, and several other paradigms.
