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Parameter tuning of a choice-function based hyperheuristic using Particle Swarm Optimization
dc.contributor.author | Crawford B. | |
dc.contributor.author | Soto R. | |
dc.contributor.author | Monfroy E. | |
dc.contributor.author | Palma W. | |
dc.contributor.author | Castro C. | |
dc.contributor.author | Paredes F. | |
dc.date.accessioned | 2020-09-02T22:15:41Z | |
dc.date.available | 2020-09-02T22:15:41Z | |
dc.date.issued | 2013 | |
dc.identifier | 10.1016/j.eswa.2012.09.013 | |
dc.identifier.citation | 40, 5, 1690-1695 | |
dc.identifier.issn | 09574174 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12728/4155 | |
dc.description | A Constraint Satisfaction Problem is defined by a set of variables and a set of constraints, each variable has a nonempty domain of possible values. Each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. A solution of the problem is defined by an assignment of values to some or all of the variables that does not violate any constraints. To solve an instance, a search tree is created and each node in the tree represents a variable of the instance. The order in which the variables are selected for instantiation changes the form of the search tree and affects the cost of finding a solution. In this paper we explore the use of a Choice Function to dynamically select from a set of variable ordering heuristics the one that best matches the current problem state in order to show an acceptable performance over a wide range of instances. The Choice Function is defined as a weighted sum of process indicators expressing the recent improvement produced by the heuristic recently used. The weights are determined by a Particle Swarm Optimization algorithm in a multilevel approach. We report results where our combination of strategies outperforms the use of individual strategies. © 2012 Elsevier Ltd. All rights reserved. | |
dc.language.iso | en | |
dc.subject | Combinatorial optimization | |
dc.subject | Constraints satisfaction | |
dc.subject | Hyperheuristics | |
dc.subject | Particle Swarm | |
dc.subject | Best match | |
dc.subject | Choice function | |
dc.subject | Constraint satisfaction problems | |
dc.subject | Constraints satisfaction | |
dc.subject | Hyper-heuristics | |
dc.subject | Hyperheuristic | |
dc.subject | Multilevel approach | |
dc.subject | Parameter-tuning | |
dc.subject | Particle swarm | |
dc.subject | Particle swarm optimization algorithm | |
dc.subject | Process indicators | |
dc.subject | Search trees | |
dc.subject | Variable ordering heuristics | |
dc.subject | Weighted Sum | |
dc.subject | Algorithms | |
dc.subject | Combinatorial optimization | |
dc.subject | Forestry | |
dc.subject | Genetic programming | |
dc.subject | Particle swarm optimization (PSO) | |
dc.subject | Algorithms | |
dc.subject | Genetic Engineering | |
dc.subject | Heuristic Methods | |
dc.subject | Optimization | |
dc.subject | Problem Solving | |
dc.subject | Restraints | |
dc.title | Parameter tuning of a choice-function based hyperheuristic using Particle Swarm Optimization | |
dc.type | Article |