Artificial Bee Colony Algorithm Based on Local Information Sharing in Dynamic Environment

2015 ◽  
pp. 627-641 ◽  
Author(s):  
Ryo Takano ◽  
Tomohiro Harada ◽  
Hiroyuki Sato ◽  
Keiki Takadama
Keyword(s):  
Information Sharing ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Local Information ◽  
Bee Colony

scholarly journals Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

2016 ◽  
Vol 2016 ◽  
pp. 1-18
Author(s):  
Linguo Li ◽  
Lijuan Sun ◽  
Jian Guo ◽  
Chong Han ◽  
Shujing Li
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Differential Evolution Algorithm ◽  
Direct Methods ◽  
Information Aggregation ◽  
Local Information ◽  
Fuzzy Entropy ◽  
Running Time ◽  
Bee Colony ◽  
Kapur’S Entropy

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur’s entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image’s spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur’s entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

Dynamic Function Optimization by Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3562-3566 ◽  
Author(s):  
Shuo Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Greedy Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Dynamic Function

In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

Self-Adaptive and Adaptive Parameter Control in Improved Artificial Bee Colony Algorithm

Informatica ◽  
2017 ◽  
Vol 28 (3) ◽  
pp. 415-438 ◽  
Author(s):  
Bekir Afşar ◽  
Doğan Aydin ◽  
Aybars Uğur ◽  
Serdar Korukoğlu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Parameter Control ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Adaptive Parameter Control ◽  
Self Adaptive
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