Advanced search
Start date
Betweenand


Intensification, learning and diversification in a hybrid metaheuristic: an efficient unification

Full text
Author(s):
Maximo, Vinicius R. ; Nascimento, Maria C. V.
Total Authors: 2
Document type: Journal article
Source: Journal of Heuristics; v. 25, n. 4-5, p. 26-pg., 2019-10-01.
Abstract

Hybrid heuristic methods have lately been pointed out as an efficient approach to combinatorial optimization problems. The main reason behind this is that, by combining components from different metaheuristics, it is possible to explore solutions (which would be unreachable without hybridization) in the search space. In particular, evolutionary algorithms may get trapped into local optimum solutions due to the insufficient diversity of the solutions influencing the search process. This paper presents a hybridization of the recently proposed metaheuristic-intelligent-guided adaptive search (IGAS)-with the well-known path-relinking algorithm to solve large scale instances of the maximum covering location problem. Moreover, it proposes a slight change in IGAS that was tested through computational experiments and has shown improvement in its computational cost. Computational experiments also attested that the hybridized IGAS outperforms the results found in the literature. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/21660-4 - Hibridizing heuristic and exact methods to approach combinatorial optimization problems
Grantee:Mariá Cristina Vasconcelos Nascimento Rosset
Support Opportunities: Regular Research Grants