data correcting approaches in combinatorial optimization

Data Correcting Approaches in Combinatorial Optimization

Data Correcting Approaches in Combinatorial Optimization: ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿Data Correcting Approaches in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems.  Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis  as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization  introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance  one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.¿

Combinatorial Optimization and Applications: Second

ISBN: , SKU: , AUTHOR: Yang, Boting / Du, Ding-Zhu / Wang, Cao An, PUBLISHER: Springer, This book constitutes the refereed proceedings of the Second International Conference on Combinatorial Optimization and Applications, COCOA , held in St. John's, Canada, in August . The 44 revised full papers were carefully reviewed and selected from 84 submissions. The papers feature original research in the areas of combinatorial optimization a" both theoretical issues and and applications motivated by real-world problems thus showing convincingly the usefulness and efficiency of the algorithms discussed in a practical setting. Buy Now

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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.

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