Metaheuristics:: Progress as Real Problem Solvers
Toshihide Ibaraki, Koji Nonobe, Mutsunori Yagiura
Springer Science & Business Media, Mar 30, 2006 - Mathematics - 414 pages
Our globalized world brings us increasing complexity and many computationally hard problems. Metaheuristics are mathematical optimization methods that have become a powerful answer to many of these difficult problems. As a growing set of robust methods, Metaheuristics is producing effective algorithms that compute approximate solutions of high quality in realistic computational time.
METAHEURISTICS: Progress as Real Problem Solvers is a peer-reviewed volume of eighteen current, cutting-edge papers by leading researchers in the field. Included are an invited paper by F. Glover and G. Kochenberger, which discusses the concept of Metaheuristic agent processes, and a tutorial paper by M.G.C. Resende and C.C. Ribeiro discussing GRASP with path-relinking. Other papers discuss problem-solving approaches to timetabling, automated planograms, elevators, space allocation, shift design, cutting stock, flexible shop scheduling, colorectal cancer and cartography. A final group of methodology papers clarify various aspects of Metaheuristics from the computational view point.
The volume's objective is to consolidate works in operations research, management science, artificial intelligence, computer science, and related fields to further the understanding of basic principles and the developing domain of Metaheuristics. This includes genetic algorithms, simulated annealing, tabu search, evolutionary computation, greedy randomized adaptive search procedures (GRASP), scatter search, ant system, variable neighborhood search, guided local search, iterated local search, noising methods, threshold accepting, memetic algorithms, neural networks, and other hybrid and/or variant approaches for solving hard combinatorial problems.
What people are saying - Write a review
Papers on Problem Solving
An Investigation of Automated Planograms Using a Simulated An 87
Validation and Optimization of an Elevator Simulation Model with
Local Search Algorithms for the TwoDimensional Cutting Stock
Papers on Methodologies
Speeding Up Local Search Neighborhood Evaluation for a Mu1ti
Consistent Neighbourhood in a Tabu Search 369