Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
A study of diversification strategies for the quadratic assignment problem
Computers and Operations Research - Special issue: heuristic, genetic and tabu search
Reactive search, a history-sensitive heuristic for MAX-SAT
Journal of Experimental Algorithmics (JEA)
Rank-Two Relaxation Heuristics for MAX-CUT and Other Binary Quadratic Programs
SIAM Journal on Optimization
A Semidefinite Programming Based Polyhedral Cut and Price Approach for the Maxcut Problem
Computational Optimization and Applications
On greedy construction heuristics for the MAX-CUT problem
International Journal of Computational Science and Engineering
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
Advanced Scatter Search for the Max-Cut Problem
INFORMS Journal on Computing
Competitive simulated annealing and Tabu Search algorithms for the max-cut problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving the maxcut problem by the global equilibrium search
Cybernetics and Systems Analysis
A study of breakout local search for the minimum sum coloring problem
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A study of adaptive perturbation strategy for iterated local search
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Breakout local search for the vertex separator problem
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Given an undirected graph G=(V,E) where each edge of E is weighted with an integer number, the maximum cut problem (Max-Cut) is to partition the vertices of V into two disjoint subsets so as to maximize the total weight of the edges between the two subsets. As one of Karp's 21 NP-complete problems, Max-Cut has attracted considerable attention over the last decades. In this paper, we present Breakout Local Search (BLS) for Max-Cut. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. The proposed algorithm shows excellent performance on the set of well-known maximum cut benchmark instances in terms of both solution quality and computational time. Out of the 71 benchmark instances, BLS is capable of finding new improved results in 34 cases and attaining the previous best-known result for 35 instances, within computing times ranging from less than 1s to 5.6h for the largest instance with 20,000 vertices.