A quick artificial bee colony (qABC) algorithm and its performance on optimization problems

Karaboğa, Derviş; Görkemli, Beyza

doi:10.1016/j.asoc.2014.06.035

Cited by 282 publications

(131 citation statements)

References 16 publications

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“…In this research, the stated optimization problem is solved with five different metaheuristic algorithms: Artificial Bee Colony (ABC) developed by Karaboga and Basturk [6], quick Artificial Bee Colony (qABC) proposed by Karaboga and Gorkemli [7], Ant Colony Optimization for real numbers (ACOr) developed by Socha and Dorigo [24], Modified Differential Evolution (MDE) developed by Angira and Babu [1], and a Simulated Annealing (SA) algorithm proposed by Bohachevsky, et al [2]. A generalized discrete variables handling method that was proposed and implemented by Liao [13], is incorporated in all the algorithms in this research.…”

Section: Methodsmentioning

confidence: 99%

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Hossain¹

2017

IJARA

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In die manufacturing industries surface roughness is considered as a vital quality characteristic in order to retain the consumers' satisfaction. On the other hand, manufacturers want to minimize the machining time which eventually reduces their cost. This research deals with an optimization problem to minimize the machining time (T) for end milling operation on hot die steel (H13), subject to specified surface roughness (R a ) limits. Six machining parameters and corresponding T and R a were recorded from 74 independent experiments. After exhaustive search, three machining parameters (tool inclination angle, tool diameter and radial depth of cut) for R a and two machining parameters (feed rate and radial depth of cut) for T are found to be highly influential. In terms of these corresponding parameters, two ANFIS models are developed for the prediction of R a and T, respectively. These models are utilized to find the optimum values of machining parameters. Five advanced metaheuristic algorithms, Artificial Bee Colony (ABC), quick artificial bee colony, modified differential evolution, ant colony optimization for real numbers and simulated annealing, with or without local search, are applied for solving this optimization problem. Each of the algorithms is run for 30 times, allowing 100,000 number of function evaluations in each run. Statistical analysis (F-test, t-test) are done to evaluate the performance of the algorithms. Hybrid ABC with local search is proposed as the best algorithm for solving this problem based on average of minimum machining time obtained. The proposed optimization approach can be used for parameter selection in real time machining with artificially intelligent Computer Numerical Control (CNC) machine tools.

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Section: Methodsmentioning

confidence: 99%

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Hossain¹

2017

IJARA

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“…Therefore, involving ABC in our numerical experiments is meaningful. Note that although several modified versions of ABC algorithms are available, e.g., [19,20], only the standard ABC is involved in our research.…”

Section: The Abc Algorithmmentioning

confidence: 99%

“…u(x i , 5, 100, 4), x ∈ [−50, 50] n , (20) where y i and u are defined as in f 12 . It is obvious that all the three methods have reached the global optimum for all the 6 functions.…”

Section: B 30-d Multimodal Functionsmentioning

confidence: 99%

Splitting for optimization

Duan

Kroese

2016

Computers & Operations Research

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The splitting method is a well-known method for rare-event simulation, where sample paths of a Markov process are split into multiple copies during the simulation, so as to make the occurrence of a rare event more frequent. Motivated by the splitting algorithm we introduce a novel global optimization method for continuous optimization that is both very fast and accurate. Numerical experiments demonstrate that the new splitting-based method outperforms known methods such as the differential evolution and artificial bee colony algorithms for many bench mark cases.

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“…Upgraded ABC introduced by Brajevic in [15] for constraint problems. Quick ABC that called qABC used neighborhood radius for optimization local search do by onlooker bees [16]. A binary ABC introduced by Hancer for feature selection [17].…”

Section: Related Workmentioning

confidence: 99%