Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization Review

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(More customer reviews)Once one has read the articles in this book it becomes clear that meta-heuristics are intelligently designed methods to provide decision makers with tools for decision support. Each chapter is self-contained and provides different insights into specific methods (especially genetic algorithms, neural networks, tabu search, simulated annealing). These methods are well explored and explained by means of theoretical as well as practical results, e.g., for vehicle routing or mail delivery or generalized assignment. Ideas on how to implement the methods are also provided. Most papers are easy to read with only some preliminaries in mathematics (or combinatorial optimization). The chapters are carefully collected and could have been accepted for high-quality journals as well. Well done.

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Meta-Heuristics: Advances and Trends in Local SearchParadigms for Optimizations comprises a carefully refereedselection of extended versions of the best papers presented at theSecond Meta-Heuristics Conference (MIC 97). The selected articlesdescribe the most recent developments in theory and applications ofmeta-heuristics, heuristics for specific problems, and comparativecase studies. The book is divided into six parts, grouped mainly bythe techniques considered. The extensive first part with twelve paperscovers tabu search and its application to a great variety ofwell-known combinatorial optimization problems (including theresource-constrained project scheduling problem and vehicle routingproblems). In the second part we find one paper where tabu search andsimulated annealing are investigated comparatively and two paperswhich consider hybrid methods combining tabu search with geneticalgorithms. The third part has four papers on genetic and evolutionaryalgorithms. Part four arrives at a new paradigm withinmeta-heuristics. The fifth part studies the behavior of parallel localsearch algorithms mainly from a tabu search perspective. The finalpart examines a great variety of additional meta-heuristics topics,including neural networks and variable neighbourhood search as well asguided local search. Furthermore, the integration of meta-heuristicswith the branch-and-bound paradigm is investigated.