Integrating the artificial bee colony and bees algorithm to face constrained optimization problems

2014 ◽  
Vol 258 ◽  
pp. 80-93 ◽  
Author(s):  
Hsing-Chih Tsai
Keyword(s):  
Constrained Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Bees Algorithm ◽  
Constrained Optimization Problems ◽  
Bee Colony

scholarly journals Constraint Consensus Based Artificial Bee Colony Algorithm for Constrained Optimization Problems

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Liling Sun ◽  
Yuhan Wu ◽  
Xiaodan Liang ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Constrained Optimization ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Constrained Optimization Problems ◽  
Constrained Problems ◽  
Bee Colony ◽  
Hybrid Heuristic Algorithm ◽  
Complex Optimization ◽  
Constraint Consensus

Over the last few decades, evolutionary algorithms (EAs) have been widely adopted to solve complex optimization problems. However, EAs are powerless to challenge the constrained optimization problems (COPs) because they do not directly act to reduce constraint violations of constrained problems. In this paper, the robustly global optimization advantage of artificial bee colony (ABC) algorithm and the stably minor calculation characteristic of constraint consensus (CC) strategy for COPs are integrated into a novel hybrid heuristic algorithm, named ABCCC. CC strategy is fairly effective to rapidly reduce the constraint violations during the evolutionary search process. The performance of the proposed ABCCC is verified by a set of constrained benchmark problems comparing with two state-of-the-art CC-based EAs, including particle swarm optimization based on CC (PSOCC) and differential evolution based on CC (DECC). Experimental results demonstrate the promising performance of the proposed algorithm, in terms of both optimization quality and convergence speed.

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