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IEEE Transactions on Evolutionary Computation, 2019
A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO’s strong global search capability and LS’s fast convergence ability.
Yulian Cao +5 more
semanticscholar +1 more source
A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO’s strong global search capability and LS’s fast convergence ability.
Yulian Cao +5 more
semanticscholar +1 more source
Computers & Operations Research, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Vaessens, R.J.M. +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Vaessens, R.J.M. +2 more
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Local search algorithm to improve the local search
14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings., 2003In this paper, we present a new cooperative framework based on using successively two local search algorithms to solve constraint satisfaction and optimization problems. Our technique is based on the integration of local search algorithms as a mechanism to diversify the search instead of using a build on diversification mechanisms. Thus we avoid tuning
M. Tounsi, P. David
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Localizer A modeling language for local search
INFORMS Journal on Computing, 1997Local search is a traditional technique to solve combinatorial search problems and has raised much interest in recent years. The design and implementation of local search algorithms is not an easy task in general and may require considerable experimentation and programming effort.
Michel, Laurent, Van Hentenryck, Pascal
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2012
At an abstract level, memetic algorithms can be seen as a broad class of populationbased stochastic local search (SLS) methods, where a main theme is "exploiting all available knowledge about a problem," see also Moscato and Cotta [618], page 105. The most wide-spread implementation of this theme is probably that of improving some or all individuals in
Montes De Oca Roldan, Marco +2 more
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At an abstract level, memetic algorithms can be seen as a broad class of populationbased stochastic local search (SLS) methods, where a main theme is "exploiting all available knowledge about a problem," see also Moscato and Cotta [618], page 105. The most wide-spread implementation of this theme is probably that of improving some or all individuals in
Montes De Oca Roldan, Marco +2 more
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Local-search Extraction of MUSes
Constraints, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gregoire, Eric +2 more
openaire +4 more sources
2017
Local search is a widely used method to solve combinatorial optimization problems. As many relevant combinatorial optimization problems are NP-hard, we often may not expect to find an algorithm that is guaranteed to return an optimal solution in a reasonable amount of time, i.e., in polynomial time.
Michiels, W., Aarts, E.H.L., Korst, Jan
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Local search is a widely used method to solve combinatorial optimization problems. As many relevant combinatorial optimization problems are NP-hard, we often may not expect to find an algorithm that is guaranteed to return an optimal solution in a reasonable amount of time, i.e., in polynomial time.
Michiels, W., Aarts, E.H.L., Korst, Jan
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Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms, 2011
We introduce CLS, for continuous local search, a class of polynomial-time checkable total functions that lies at the intersection of PPAD and PLS, and captures a particularly benign kind of local optimization in which the domain is continuous, as opposed to combinatorial, and the functions involved are continuous.
Daskalakis, Constantinos +1 more
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We introduce CLS, for continuous local search, a class of polynomial-time checkable total functions that lies at the intersection of PPAD and PLS, and captures a particularly benign kind of local optimization in which the domain is continuous, as opposed to combinatorial, and the functions involved are continuous.
Daskalakis, Constantinos +1 more
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Journal of Heuristics, 1995
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Verhoeven, M.G.A., Aarts, E.H.L.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Verhoeven, M.G.A., Aarts, E.H.L.
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