The artificial bee colony algorithm with one-position inheritance (OPIABC) has shown good performance for large-scale problems. But, the improvement in its performance for some other type test problem is not obvious, since the onlookers in this algorithm use the foraging strategy that randomly selects a neighbor to produce a new candidate. Moreover, the scout foraging behavior in this algorithm is completely random, which would sometimes make it consume more search efforts to discover some promising area and hamper its convergent speed especially for large-scale optimization. To further improve its performance, a running information-guided onlooker foraging strategy and a heuristic scout search mechanism are designed and combined with it. The improved OPIABC algorithm has been tested on a set of test functions with dimensions D = 30, 100 and 1000. Experimental results show that after using the heuristic search mechanisms, the performance of the OPIABC algorithm is significantly improved for most test problems.
This is a preview of subscription content, log in to check access.
This work was supported by the National Natural Science Foundation Program of China (61572116, 61572117, 61502089) and the Special Fund for Fundamental Research of Central Universities of Northeastern University (N161606003, N150408001, N150404009).
Compliance with ethical standards
Conflict of interest
All the authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
Akay B, Karaboga D (2012) A modified artificial bee colony algorithm for real parameter optimization. Inf Sci 192(1):120–142CrossRefGoogle Scholar
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471MathSciNetCrossRefzbMATHGoogle Scholar
Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. Appl Soft Comput 23:227–238CrossRefGoogle Scholar
Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57CrossRefGoogle Scholar
Kiran MS, Findik O (2015) A directed artificial bee colony algorithm. Appl Soft Comput 26:454–462CrossRefGoogle Scholar
Kiran MS, Hakli H, Gunduz M, Uguz H (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157MathSciNetCrossRefGoogle Scholar
Li G, Niu P, Xiao X (2012) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12(1):320–332CrossRefGoogle Scholar
Liu Y, Ling XX, Liang Y, Liu GH (2012) Improved artificial bee colony algorithm with mutual learning. J Syst Eng Electron 23(2):265–275CrossRefGoogle Scholar
Maeda M, Tsuda S (2015) Reduction of artificial bee colony algorithm for global optimization. Neurocomputing 148:70–74CrossRefGoogle Scholar
Shan H, Yasuda T, Ohkura K (2015) A self-adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems. BioSystems 132–133:43–53CrossRefGoogle Scholar
Zhang X, Yuen SY (2013) Improving artificial bee colony with one-position inheritance mechanism. Memet Comput 5(3):187–211CrossRefGoogle Scholar
Zhang S, Lee CKM, Chan HK, Choy KL, Wu Z (2015) Swarm intelligence applied in green logistics: a literature review. Eng Appl Artif Intell 37:154–169CrossRefGoogle Scholar
Zhu GP, Kwong S (2010a) Gbest-guided artificial bee colony algorithm for numerical function optimization. Math Comput 217(7):3166–3173MathSciNetzbMATHGoogle Scholar
Zhu GP, Kwong S (2010b) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217:3166–3173MathSciNetzbMATHGoogle Scholar