Modified artificial bee colony algorithm with differential evolution to enhance precision and convergence performance

•A novel modified algorithm denoted “modified ABC by DE” (mABC) is introduced.•The exploitation ability of ABC is enhanced by modifying the onlooker bee phase.•mABC is even validated by bencmarking with recent ABC variants.•The mean ranks of mABC are 1.4 and 2.3 for benchmark functions and CEC 2014....

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Bibliographic Details
Published in:Expert systems with applications Vol. 198; p. 116930
Main Authors: Ustun, Deniz, Toktas, Abdurrahim, Erkan, Uğur, Akdagli, Ali
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 15.07.2022
Elsevier BV
Subjects:
Algorithms
Artificial bee colony (ABC)
Benchmarks
Convergence
Differential evolution (DE)
Evolutionary computation
Exploitation
Heuristic methods
Hybrid algorithm
Modified ABC
Modified algorithm
Mutation
Operators (mathematics)
Optimization
Optimization algorithm
Search algorithms
Swarm intelligence
Modified algorithm
Modified ABC
Optimization algorithm
Artificial bee colony (ABC)
Hybrid algorithm
Differential evolution (DE)
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•A novel modified algorithm denoted “modified ABC by DE” (mABC) is introduced.•The exploitation ability of ABC is enhanced by modifying the onlooker bee phase.•mABC is even validated by bencmarking with recent ABC variants.•The mean ranks of mABC are 1.4 and 2.3 for benchmark functions and CEC 2014.•mABC outperforms the other variants averagely for 38 of 50 benchmark functions. Artificial bee colony (ABC) and differential evolution (DE) are the most powerful and operative meta-heuristic algorithms inspired by the nature. Although both algorithms are successful, their successes vary from phase to phase, i.e. while ABC is better in the exploration ability, DE is well in the exploitation capability. Because the diversity of mutation and exponential crossover operators is prominently better than that of onlooker bee; in this study, the exploitation ability of ABC is enhanced by replacing the onlooker bee operator with those of mutation and the crossover phases of DE in order to increase the accuracy and speed up the convergence. We hereby introduce a novel modified algorithm denoted “modified ABC by DE” (mABC). The precision performance of mABC is verified through 20 classical benchmark functions and CEC 2014 test suit by a comprehensive comparison with recent ABC variants and hybrids for 30 and 50 dimensions. The results are interpreted using various statistical evaluations such as Wilcoxon, Friedman, and Nemenyi tests. Moreover, mABC is comparatively examined over convergence plots. In concise, the mean ranks of mABC are 1.4 and 2.3 for classical benchmark functions and CEC 2014, respectively. mABC outperforms the other variants averagely for 14 of 20 classical benchmark functions and 24 of 30 CEC 2014 functions. The results manifest that the proposed mABC is a robust and reliable algorithm as well as better than the existing ABC variants and hybrids with regard to high optimization performance like precision and convergence.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.116930