A hybridization approach with predicted solution candidates for improving population-based optimization algorithms

•A hybridization approach for improving population-based meta-heuristics is proposed.•The worst candidates in a population are replaced by predicted candidates.•The calibration problem based on the population is solved to create candidates.•The hybridized ten meta-heuristics outperform their counter...

Full description

Saved in:
Bibliographic Details
Published in:Information sciences Vol. 574; pp. 133 - 161
Main Authors: Oszust, Mariusz, Sroka, Grzegorz, Cymerys, Karol
Format: Journal Article
Language:English
Published: Elsevier Inc 01.10.2021
Subjects:
Calibration problem
Global optimization
Hybridization
Inverse regression
Meta-heuristics
Population-based algorithms
Global optimization
Population-based algorithms
Hybridization
Calibration problem
Inverse regression
Meta-heuristics
ISSN:0020-0255, 1872-6291
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A hybridization approach for improving population-based meta-heuristics is proposed.•The worst candidates in a population are replaced by predicted candidates.•The calibration problem based on the population is solved to create candidates.•The hybridized ten meta-heuristics outperform their counterparts. The recent proliferation of population-based meta-heuristics designed for solving optimization problems and their successes confirm that more promising techniques inspired by physical phenomena or biological systems are desired. Therefore, in this paper, a novel hybridization approach is proposed to improve the performance of optimization algorithms. The approach replaces a small number of worst solutions obtained by a meta-heuristic with predicted candidates without altering its search operators. Specifically, a target fitness value of the predicted candidate is determined based on the fitness of the population and a search strategy. Then, a calibration problem is solved to infer its decision variables. In this study, the proposed hybridization technique is applied to ten state-of-the-art population-based algorithms. The meta-heuristics and hybrids are evaluated on 82 functions, four engineering problems, and a new challenging problem of estimating a constant in Markov’s inequality using minimal polynomials of different degrees. The experimental results reveal the superiority of the hybrids over their counterparts and confirm the suitability of the proposed approach for improving the efficiency of meta-heuristics.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2021.04.082