Parallel Combinatorial Optimization (Hardcover)

Description 

Learn to solve complex problems with efficient parallel optimization algorithms

 

This text provides an excellent balance of theory and application that enables readers to deploy powerful algorithms, frameworks, and methodologies to solve complex optimization problems in a diverse range of industries. Each chapter is written by leading experts in the fields of parallel and distributed optimization. Collectively, the contributions serve as a complete reference to the field of combinatorial optimization, including details and findings of recent and ongoing investigations.

Readers learn to solve large-scale problems quickly and efficiently with the text's clear coverage of several parallel optimization algorithms:

• Exact algorithms, including branch and bound, dynamic programming, branch and cut, semidefinite programming, and constraint programming
• Metaheuristics, including local search, tabu search, simulated annealing, scatter search, GRASP, variable neighborhood search, ant colonies, genetic programming, evolution strategies, and genetic algorithms
• Hybrid approaches, combining exact algorithms and metaheuristics
• Multi-objective optimization algorithms

The text not only presents parallel algorithms and applications, but also software frameworks and libraries that integrate parallel algorithms for combinatorial optimization. Among the well-known parallel and distributed frameworks covered are COIN, ParadisEO, BOB++, MW, and SDPARA.

Numerous real-world examples of problems and solutions demonstrate how parallel combinatorial optimization is applied in such fields as telecommunications, logistics, genomics, networking, and transportation. Whether you are a practicing engineer, field researcher, or student, this text provides you with not only the theory of parallel combinatorial optimization, but the guidance and practical tools to solve complex problems using powerful algorithms.

 

Table of Contents

Preface.

Acknowledgments.

Contributors.

1. Parallel Branch-and-Bound Algorithms (T. Crainic, B. Lecun, C. Roucairol).

2. Parallel Dynamic Programming (F. Almeida, D. Gonzalez, I. Pelaez).

3. Parallel Branch and Cut (T. Ralphs).

4. Parallel Semidefinite Programming and Combinatorial Optimization (S. J. Benson).

5. Parallel Resolution of the Satisfiability Problem: A Survey (D. Singer).

6. Parallel Metaheuristics: Algorithms and Frameworks (N. Melab, E-G. Talbi, S. Cahon, E. Alba, G. Luque).

7. Towards Parallel Design of Hybrids between Metaheuristics and Exact Methods (M. Basseur, L. Jourdan, E-G. Talbi).

8. Parallel Exact Methods for Multiobjective Combinatorial Optimization (C. Dhaenens, J. Lemesre, N. Melab, M. Mezmaz, E-G. Talbi).

9. Parallel Primal-Dual Interior Point Methods for Semidefinite Programs (M. Yamashita, K. Fujisawa, M. Fukuda, M. Kojima, K. Nakata).

10. MW: A Software Framework for Combinatorial Optimization on Computational Grids (W. Glankwamdee, T. Linderoth).

11. Constraint Logic Programming on Multiple Processors (I. Sakellariou, I. Vlahavas).

12. Application of Parallel Metaheuristics to Optimization Problems in Telecommunications and Bioinformatics (S. L. Martins, C. Ribeiro, I. Rosseti).

Index.

學習使用高效的並行優化算法解決複雜問題

這本書提供了理論和應用的完美平衡，讓讀者能夠在各種不同行業中應用強大的算法、框架和方法來解決複雜的優化問題。每一章都由並行和分散優化領域的領先專家撰寫，共同作為組合優化領域的完整參考，包括最近和正在進行的研究的細節和發現。

讀者將學習使用本書清晰介紹的多種並行優化算法快速高效地解決大規模問題，包括：

- 精確算法，包括分支界限法、動態規劃、分支剪枝、半定規劃和約束規劃
- 元啟發式算法，包括局部搜索、禁忌搜索、模擬退火、散射搜索、GRASP、變鄰域搜索、螞蟻群算法、遺傳編程、進化策略和遺傳算法
- 混合方法，結合精確算法和元啟發式算法
- 多目標優化算法

本書不僅介紹了並行算法和應用，還介紹了將並行算法應用於組合優化的軟件框架和庫。其中包括COIN、ParadisEO、BOB++、MW和SDPARA等知名的並行和分散框架。書中還提供了許多實際問題和解決方案的例子，展示了並行組合優化在各個領域的應用。