Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Abstract

Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay's ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more considering mean solutions.

Constrained artificial bee colony algorithm for optimization problems

“…In addition, smart flight operator is employed to be used in scout bee instead of the random selection mechanism in original ABC. The numerical results showed that M-ABC provides comparable results with respect to algorithms under comparison [19].…”

“…Mezura-Montes and Cetina-Domínguez [19] presented a modified ABC (M-ABC). Compared with the original constrained ABC four modifications on the selection mechanism, the equality and boundary constraints, and scout bee operators are presented.…”

“…The proposed cABC is evaluated and The numerical performance of cABC algorithm was compared with original ABC [3], MABC [17], M-ABC [18], SF-ABC [16], and MO-ABC [19] algorithms. From Table 1 it is obvious that the cABC algorithm in Problems g02, g03, g04, g05, g07, g08, g09 and g11 outperforms other algorithms.…”

An Approach to Fuzzy Modeling of Electromagnetic Actuated Clutch Systems

“…Moreover, the convergence of the modeling error to zero can be achieved by the optimization of the parameters of several parameters of the fuzzy models including input membership functions or parameters in the rule consequents. Various optimization algorithms can be implemented in this context [28], [29], [30], [31], [32], [33], [34], [35], [36]. …”

Gri Kurt Optimizasyon Algoritmasına Dayanan Çok Seviyeli İmge Eşik Seçimi

“…Meta-sezgisel algoritmaların orijinal versiyonları, çözüm kalitesini artırmak için modifiye edilir veya melezleştirilir. En yaygın doğadan esinlenen algoritmalar, parçacık sürü optimizasyonu (PSO) [18], diferansiyel evrim (DE) [19], ateşböceği algoritması (FA) [20], [21], guguk kuşu arama (CS) [22], karınca koloni optimizasyonu [23][24][25][26], yapay arı koloni algoritması [27][28][29][30], yarasa algoritması (BA) [31], ağaç tohum algoritması [32] …”