Wiley Encyclopedia of Operations Research and Management Science 2011
DOI: 10.1002/9780470400531.eorms1107
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Continuous Optimization by Variable Neighborhood Search

Abstract: Variable neighborhood search (VNS) is a metaheuristic for solving optimization problems, whose basic idea is a systematic change of neighborhood structures in the search for a better solution. During the last 15 years, many variants of VNS have been proposed. In this section, we first describe basic VNS procedures and then discuss some recent extensions for solving continuous global optimization problems.

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Cited by 3 publications
(3 citation statements)
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“…Various mathematical models and tools are used in the theoretical analysis of metaheuristics. For example, probability theory is used in the analysis [25], convergence analysis uses Markov chain analysis [26], etc.…”
Section: Metaheuristic Optimizationmentioning
confidence: 99%
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“…Various mathematical models and tools are used in the theoretical analysis of metaheuristics. For example, probability theory is used in the analysis [25], convergence analysis uses Markov chain analysis [26], etc.…”
Section: Metaheuristic Optimizationmentioning
confidence: 99%
“…• In the case that x ′′ is worse than x and n = n max , the algorithm continues with the old solution x and the neighborhood is set to n = n min . The convergence of VNS is proven in the paper [26]. VNS is successfully applied to various NP-hard optimization problems: metric dimension and minimal doubleresolving set [15], multiple level warehouse layout [19] multimode set covering [40], bandwidth coloring [18], precedence-constrained colored traveling salesman [41], constrained shortest paths [42], multi-objective community detection [43], etc.…”
Section: Algorithm 1 Variable Neighbourhood Searchmentioning
confidence: 99%
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