A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning

2015 ◽  
Vol 28 (3) ◽  
pp. 1023-1037 ◽  
Author(s):  
Bing Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Bee Colony ◽  
Opposition Based Learning

Artificial bee colony algorithm with modified search strategy

2013 ◽  
Vol 32 (12) ◽  
pp. 3326-3330
Author(s):  
Yin-xue ZHANG ◽  
Xue-min TIAN ◽  
Yu-ping CAO
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Bee Colony

scholarly journals A Multistrategy Artificial Bee Colony Algorithm Enlightened by Variable Neighborhood Search

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Wan-li Xiang ◽  
Yin-zhen Li ◽  
Rui-chun He ◽  
Xue-lei Meng ◽  
Mei-qing An
Keyword(s):  
Variable Neighborhood Search ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Random Search ◽  
Search Strategies ◽  
Neighborhood Change ◽  
Neighborhood Search ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Change Of Variable

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.

Inferring the atmospheric duct from radar sea clutter using the improved artificial bee colony algorithm

2018 ◽  
Vol 10 (4) ◽  
pp. 437-445 ◽  
Author(s):  
Chao Yang ◽  
Lixin Guo
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Orthogonal Experimental Design ◽  
Sea Clutter ◽  
Particle Swarm Optimizer ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct ◽  
Comparison Results ◽  
Better Than

AbstractIn this paper, an orthogonal crossover artificial bee colony (OCABC) algorithm based on orthogonal experimental design is presented and applied to infer the marine atmospheric duct using the refractivity from clutter technique, and the radar sea clutter power is simulated by the commonly used parabolic equation method. In order to test the accuracy of the OCABC algorithm, the measured data and the simulated clutter power with different noise levels are, respectively, utilized to estimate the evaporation duct and surface duct. The estimation results obtained by the proposed algorithm are also compared with those of the comprehensive learning particle swarm optimizer and the artificial bee colony algorithm combined with opposition-based learning and global best search equation. The comparison results demonstrate that the performance of proposed algorithm is better than those of the compared algorithms for the marine atmospheric duct estimation.

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