A novel multi-objective optimisation algorithm: artificial bee colony in conjunction with bacterial foraging

2015 ◽  
Vol 3 (4) ◽  
pp. 369 ◽  
Author(s):  
Mohammad Javad Mahmoodabadi ◽  
Milad Taherkhorsandi ◽  
Rahmat Abedzadeh Maafi ◽  
Krystel K. Castillo Villar
Keyword(s):  
Artificial Bee Colony ◽  
Multi Objective ◽  
Bacterial Foraging ◽  
Optimisation Algorithm ◽  
Bee Colony

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

2011 ◽  
Vol 11 (1) ◽  
pp. 489-499 ◽  
Author(s):  
S.N. Omkar ◽  
J. Senthilnath ◽  
Rahul Khandelwal ◽  
G. Narayana Naik ◽  
S. Gopalakrishnan
Keyword(s):  
Design Optimization ◽  
Composite Structures ◽  
Artificial Bee Colony ◽  
Multi Objective ◽  
Bee Colony

Elite-guided multi-objective artificial bee colony algorithm

2015 ◽  
Vol 32 ◽  
pp. 199-210 ◽  
Author(s):  
Ying Huo ◽  
Yi Zhuang ◽  
Jingjing Gu ◽  
Siru Ni
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Multi Objective ◽  
Bee Colony

Multi-Objective Artificial Bee Colony for scheduling in Grid environments

2011 ◽  
Author(s):  
Maria Arsuaga-Rios ◽  
Miguel A. Vega-Rodriguez ◽  
Francisco Prieto-Castrillo
Keyword(s):  
Artificial Bee Colony ◽  
Multi Objective ◽  
Bee Colony ◽  
Grid Environments

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

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