The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
A new heuristic algorithm solving the linear ordering problem
Computational Optimization and Applications
Intensification and diversification with elite tabu search solutions for the linear ordering problem
Computers and Operations Research
Tabu Search
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Scatter Search with Random Walk Strategy for SAT and MAX-W-SAT Problems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A scatter search approach for the minimum sum-of-squares clustering problem
Computers and Operations Research
Minimizing Labor Requirements in a Periodic Vehicle Loading Problem
Computational Optimization and Applications
Computers and Operations Research - Articles presented at the conference on routing and location (CORAL)
Journal of Global Optimization
Variable neighborhood search for the linear ordering problem
Computers and Operations Research
Context-Independent Scatter and Tabu Search for Permutation Problems
INFORMS Journal on Computing
Multilevel algorithms for linear ordering problems
Journal of Experimental Algorithmics (JEA)
Evolving feasible linear ordering problem solutions
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Computers and Operations Research - Articles presented at the conference on routing and location (CORAL)
Hybrid heuristics for the maximum diversity problem
Computational Optimization and Applications
Differential Evolution and Genetic Algorithms for the Linear Ordering Problem
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Scatter search technique for exam timetabling
Applied Intelligence
Tabu search for the linear ordering problem with cumulative costs
Computational Optimization and Applications
Revised GRASP with path-relinking for the linear ordering problem
Journal of Combinatorial Optimization
Computers and Operations Research
Cryptanalysis of substitution ciphers using scatter search
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
A scatter search algorithm for the slab stack shuffling problem
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
3D inter-subject medical image registration by scatter search
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
A benchmark library and a comparison of heuristic methods for the linear ordering problem
Computational Optimization and Applications
A genetic programming approach for solving the linear ordering problem
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Scatter Search Applied to the Vehicle Routing Problem with Simultaneous Delivery and Pickup
International Journal of Applied Metaheuristic Computing
A Complementary Cyber Swarm Algorithm
International Journal of Swarm Intelligence Research
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Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear global optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as in generating surrogate constraints, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. In this paper, we present a scatter search implementation designed to find high quality solutions for the NP-hard linear ordering problem, which has a significant number of applications in practice. The LOP, for example, is equivalent to the so-called triangulation problem for input-output tables in economics. Our implementation incorporates innovative mechanisms to combine solutions and to create a balance between quality and diversification in the reference set. We also use a tracking process that generates solution statistics disclosing the nature of combinations and the ranks of antecedent solutions that produced the best final solutions. Extensive computational experiments with more than 300 instances establishes the effectiveness of our procedure in relation to approaches previously identified to be best.