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Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.
List of contents
Advances for New Model and Solution Approaches.- A Scatter Search Tutorial for Graph-Based Permutation Problems.- A Multistart Scatter Search Heuristic for Smooth NLP and MINLP Problems.- Scatter Search Methods for the Covering Tour Problem.- Solution of the SONET Ring Assignment Problem with Capacity Constraints.- Advances for Solving Classical Problems.- A Very Fast Tabu Search Algorithm for Job Shop Problem.- Tabu Search Heuristics for the Vehicle Routing Problem.- Some New Ideas in TS for Job Shop Scheduling.- A Tabu Search Heuristic for the Uncapacitated Facility Location Problem.- Adaptive Memory Search Guidance for Satisfiability Problems.- Experimental Evaluations.- Lessons from Applying and Experimenting with Scatter Search.- Tabu Search for Mixed Integer Programming.- Scatter Search vs. Genetic Algorithms.- Review of Recent Developments.- Parallel Computation, Co-operation, Tabu Search.- Using Group Theory to Construct and Characterize Metaheuristic Search Neighborhoods.- Logistics Management.- New Procedural Designs.- On the Integration of Metaheuristic Strategies in Constraint Programming.- General Purpose Metrics for Solution Variety.- Controlled Pool Maintenance for Metaheuristics.- Adaptive Memory Projection Methods for Integer Programming.- RAMP: A New Metaheuristic Framework for Combinatorial Optimization.
Summary
Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.
