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Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

Authors :
Liling Sun
Yuhan Wu
Xiaodan Liang
Maowei He
Hanning Chen
Source :
Discrete Dynamics in Nature and Society, Vol 2019 (2019)
Publication Year :
2019
Publisher :
Wiley, 2019.

Abstract

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

Subjects

Subjects :
Mathematics
QA1-939

Details

Language :
English
ISSN :
10260226 and 1607887X
Volume :
2019
Database :
Directory of Open Access Journals
Journal :
Discrete Dynamics in Nature and Society
Publication Type :
Academic Journal
Accession number :
edsdoj.b99361f6d04744fcba86f84b676f569a
Document Type :
article
Full Text :
https://doi.org/10.1155/2019/6523435