A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems

By Dervis Karaboga, Bahriye Akay. Applied Soft Computing, 2011

1,629 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/715dab315f388bc0ed7d1276cb7848a26399189b

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Citations in the corpus

A Comprehensive Survey of Clustering Algorithms (2015, neutral): “Typical algorithms include the 4 main categories: ACO_based [96,97], PSO_based [97,98], SFLA_based [99] and ABC_based [100].”
Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “The performance of this algorithm (Karaboga & Basturk, 2008; Karaboga & Ozturk, 2011) is very good in terms of the local and the global optimization due to the selection schemes employed and the neighbour production mechanism used.”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, methodology): “Karaboga and Basturk [91] and Karaboga andAkay [92] changed a greedy selection based only on the objective function values by the use of feasibility rules with the aim of adapting an artificial bee colony algorithm (ABC) to solve CNOPs.”
Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems (2012, neutral): “Some of the well known meta-heuristics developed during the last three decades are: Genetic Algorithm (GA) [12] which works on the principle of the Darwinian theory of the survival of the fittest and the theory of evolution of the living beings; Artificial Immune Algorithm (AIA) [9] which works on t”
Enhanced Energy Output From a PV System Under Partial Shaded Conditions Through Artificial Bee Colony (2015, methodology): “In this context, this paper develops artificial bee colony (ABC) optimization algorithm for GMPP tracking in a PV system under PSC. ABC algorithm simulates the intelligent foraging behavior of a honeybee swarm, which was first proposed by Karaboga and Ozturk [13].”
A Survey on Deep Learning in Big Data (2017, background): “In a classification problem, we are instead trying to predict results into discrete outputs [32].”
A comprehensive review of firefly algorithms (2013, neutral): “The performance of FA for clustering was compared with the results of other nature-inspired algorithms like artiﬁcial bees colony (ABC) [7] and particle swarm intelligence (PSO) [6], and and other nine methods used in the literature on the 13 test data sets from literature [133] [134].”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, neutral): “Ilter AIS – [235], ABC – [250], Firefly – [278],”
Black hole: A new heuristic optimization approach for data clustering (2013, methodology): “For instance, simulated annealing [33], tabu search [58], genetic algorithms [57,62,63,66], ant colony optimization [28,67,89], neural gas algorithm [72–74], honey bee mating optimization [23], differential evolution algorithm [15,16], particle swarm optimization algorithm [1,44,54], artificial bee ”
Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems (2016, neutral): “The number of design variables and their ranges are different for each problem (Liang et al., 2006; Karaboga and Basturk, 2007; Karaboga and Akay, 2011).”