Shape Matching Optimization via Atomic Potential Function and Artificial Bee Colony Algorithms with Various Search Strategies

2015 ◽  
Author(s):  
Bai Li ◽  
Hongxin Cao ◽  
Mandong Hu ◽  
Changjun Zhou
Keyword(s):  
Potential Function ◽  
Shape Matching ◽  
Artificial Bee Colony ◽  
Search Strategies ◽  
Atomic Potential ◽  
Bee Colony

Artificial bee colony algorithm with multiple search strategies

2015 ◽  
Vol 271 ◽  
pp. 269-287 ◽  
Author(s):  
Wei-feng Gao ◽  
Ling-ling Huang ◽  
San-yang Liu ◽  
Felix T.S. Chan ◽  
Cai Dai ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategies ◽  
Bee Colony

Artificial Bee Colony with enhanced search strategies for food sources

2016 ◽  
pp. 1187-1191
Author(s):  
Sheng-Ta Hsieh ◽  
Chun-Ling Lin ◽  
Shih-Yuan Chiu
Keyword(s):  
Artificial Bee Colony ◽  
Search Strategies ◽  
Food Sources ◽  
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.

scholarly journals A New Artificial Bee Colony Based on Multiple Search Strategies and Dimension Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 133982-133995 ◽  
Author(s):  
Songyi Xiao ◽  
Wenjun Wang ◽  
Hui Wang ◽  
Xinyu Zhou
Keyword(s):  
Artificial Bee Colony ◽  
Search Strategies ◽  
Bee Colony

scholarly journals Hybridization of Artificial Bee Colony Algorithm and its Variants with Hyperbolic Spiral based Local Search

2019 ◽  
Vol 9 (2) ◽  
pp. 3547-3551
Keyword(s):  
Local Search ◽  
Honey Bees ◽  
Efficient Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategies ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Swarming Behavior

Artificial bee colony (ABC) algorithm is grounded on intelligent swarming behavior of honey bees. It is one of the efficient algorithm for optimization. The ABC algorithm is good in exploration and sometimes fails to exploit properly. Local search strategies in addition to existing steps play important role to improve exploitation. In order to improve exploitation here a local search inspired by the nature of hyperbolic spiral introduced in ABC. The purposed variant used with ABC, Best-so-far ABC and Gbest ABC. Outcomes proved that hybrid of these algorithms with hyperbolic search gives good results with higher accuracy and reliability.

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