Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

2007 ◽  
pp. 789-798 ◽  
Author(s):  
Dervis Karaboga ◽  
Bahriye Basturk
Keyword(s):  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Bee Colony

scholarly journals Artificial bee colony algorithm variants on constrained optimization

2017 ◽  
Vol 7 (1) ◽  
pp. 98-111 ◽  
Author(s):  
Bahriye Basturk Akay ◽  
Dervis Karaboga
Keyword(s):  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Abc Algorithm ◽  
Constrained Problems ◽  
Bee Colony ◽  
The Stability ◽  
Feasible Space

Optimization problems are generally classified into two main groups:unconstrained and constrained. In the case of constrainedoptimization, special techniques are required to handle withconstraints and produce solutions in the feasible space. Intelligentoptimization techniques that do not make assumptions on the problemcharacteristics are preferred to produce acceptable solutions to theconstrained optimization problems. In this study, the performance ofartificial bee colony algorithm (ABC), one of the intelligentoptimization techniques, is investigated on constrained problems andthe effect of some modifications on the performance of the algorithmis examined. Different variants of the algorithm have been proposedand compared in terms of efficiency and stability. Depending on theresults, when DE operators were integrated into ABC algorithm'sonlooker phase while the employed bee phase is retained as in ABCalgorithm, an improvement in the performance was gained in terms ofthe best solution in addition to preserving the stability of thebasic ABC. The ABC algorithm is a simple optimization algorithm thatcan be used for constrained optimization without requiring a prioriknowledge.

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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