Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

Álvaro Gómez-Rubio, Ricardo Soto, Broderick Crawford, Adrián Jaramillo, David Mancilla, Carlos Castro, Rodrigo Olivares

Research output: Contribution to journalArticlepeer-review

Abstract

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in ad-equate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.

Original languageEnglish
Article number274
JournalMathematics
Volume10
Issue number2
DOIs
StatePublished - 1 Jan 2022
Externally publishedYes

Keywords

  • Big data clustering
  • Distributed metaheuristics
  • Optimization problems
  • Parallel metaheuristic

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