Crossover-based artificial bee colony algorithm for constrained optimization problems

2015 ◽  
Vol 26 (7) ◽  
pp. 1587-1601 ◽  
Author(s):  
Ivona Brajevic
Keyword(s):  
Constrained Optimization ◽  
Artificial Bee Colony 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.

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