Global search in single-solution-based metaheuristics
Data Technologies and Applications
ISSN: 2514-9288
Article publication date: 12 March 2020
Issue publication date: 7 July 2020
Abstract
Purpose
Metaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.
Design/methodology/approach
In this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.
Findings
The proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.
Originality/value
In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.
Keywords
Acknowledgements
This work was supported by the Ministry of Education, Malaysia (grant number FRGS/1/2019/ICT02/UKM/01/1), and the Universiti Kebangsaan Malaysia (grant number DIP-2016-024).
Citation
Jaddi, N.S. and Abdullah, S. (2020), "Global search in single-solution-based metaheuristics", Data Technologies and Applications, Vol. 54 No. 3, pp. 275-296. https://doi.org/10.1108/DTA-07-2019-0115
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited