A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Core Problems in Knapsack Algorithms
Operations Research
Grasp and Path Relinking for 2-Layer Straight Line Crossing Minimization
INFORMS Journal on Computing
A Hybrid GRASP with Perturbations for the Steiner Problem in Graphs
INFORMS Journal on Computing
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Computers and Operations Research
Adaptive memory search for multidemand multidimensional knapsack problems
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Efficient simulation-based discrete optimization
WSC '04 Proceedings of the 36th conference on Winter simulation
A Local-Search-Based Heuristic for the Demand-Constrained Multidimensional Knapsack Problem
INFORMS Journal on Computing
Context-Independent Scatter and Tabu Search for Permutation Problems
INFORMS Journal on Computing
Hybridizing the cross-entropy method: An application to the max-cut problem
Computers and Operations Research
Advanced Scatter Search for the Max-Cut Problem
INFORMS Journal on Computing
Hybrid heuristics for the maximum diversity problem
Computational Optimization and Applications
A black-box scatter search for optimization problems with integer variables
Journal of Global Optimization
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The purpose of this paper is to apply the scatter search methodology to general classes of binary problems. We focus on optimization problems for which the solutions are represented as binary vectors and that may or may not include constraints. Binary problems arise in a variety of settings, including engineering design and statistical mechanics (e.g., the spin glass problem). A distinction is made between two sets of general constraint types that are handled directly by the solver and other constraints that are addressed via penalty functions. In both cases, however, the heuristic treats the objective function evaluation as a black box. We perform computational experiments with four well-known binary optimization problems to study the efficiency (speed) and effectiveness (solution quality) of the proposed method. Comparisons are made against both commercial software and specialized procedures on a set of 376 instances. We chose commercial software that is similar in nature to the proposed procedure, namely, it treats the objective function as a black box and the search is based on evolutionary optimization techniques.