DMC-GRASP: A continuous GRASP hybridized with data mining
Loading...
Files
Date
2022-09-06
Authors
Santos, Raphael Gomes
Plastino, Alexandre
de Oliveira, Alexandre C. M.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
The hybridization of metaheuristics with data mining techniques has been successfully applied to combinatorial optimization problems. Examples of this type of strategy are DM-GRASP and MDM-GRASP, hybrid versions of the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, which incorporate data mining techniques. This type of hybrid method is called Data-Driven Metaheuristics and aims at extracting useful knowledge from the data generated by metaheuristics in their search process. Despite success in combinatorial problems like the set packing problem and maximum diversity problem, proposals of this type to solve continuous optimization problems are still scarce in the literature. This work presents a data mining hybrid version of C-GRASP, an adaptation of GRASP for problems with continuous variables. We call this new version DMC-GRASP, which identifies patterns in high-quality solutions and generates new solutions guided by these patterns. We performed computational experiments with DMC-GRASP on a set of well-known mathematical benchmark functions, and the results showed that metaheuristics for continuous optimization could also benefit from using patterns to guide the search for better solutions.
Description
Keywords
Continuous optimization , Data mining , Metaheuristics
Citation
Santos, R. G., Plastino, A., de Oliveira, A. C. M. (2022) 'DMC-GRASP: A continuous GRASP hybridized with data mining', 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, 18-23 July. doi: 10.1109/CEC55065.2022.9870264
Link to publisher’s version
Collections
Copyright
© 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.