Journal ArticleDOI
A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
TLDR
Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters.About:
This article is published in Applied Mathematics and Computation.The article was published on 2009-08-01. It has received 2835 citations till now. The article focuses on the topics: Artificial bee colony algorithm & Meta-optimization.read more
Citations
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Journal ArticleDOI
A comprehensive survey: artificial bee colony (ABC) algorithm and applications
TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Journal ArticleDOI
Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems
TL;DR: The proposed algorithm is found to secure first rank for the ‘best’ and ‘mean’ solutions in the Friedman’s rank test for all the 24 constrained benchmark problems.
Journal ArticleDOI
Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems
TL;DR: An efficient optimization method called 'Teaching-Learning-Based Optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions.
Journal ArticleDOI
Gbest-guided artificial bee colony algorithm for numerical function optimization
Guopu Zhu,Sam Kwong +1 more
TL;DR: An improved ABC algorithm called gbest-guided ABC (GABC) algorithm is proposed by incorporating the information of global best (gbest) solution into the solution search equation to improve the exploitation of ABC algorithm.
Journal ArticleDOI
Backtracking Search Optimization Algorithm for numerical optimization problems
TL;DR: The Wilcoxon Signed-Rank Test is used to statistically compare BSA's effectiveness in solving numerical optimization problems with the performances of six widely used EA algorithms: PSO, CMAES, ABC, JDE, CLPSO and SADE and shows that in general, BSA can solve the benchmark problems more successfully than the comparison algorithms.
References
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Book
New Ideas In Optimization
David Corne,Marco Dorigo,Fred Glover,Dipankar Dasgupta,Pablo Moscato,Riccardo Poli,Kenneth V. Price +6 more
TL;DR: The techniques treated in this text represent research as elucidated by the leaders in the field and are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search.
Proceedings ArticleDOI
Defining a Standard for Particle Swarm Optimization
D. Bratton,James Kennedy +1 more
TL;DR: A standard algorithm is defined here which is designed to be a straightforward extension of the original algorithm while taking into account more recent developments that can be expected to improve performance on standard measures.
Proceedings ArticleDOI
A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems
J.S. Vesterstrom,René Thomsen +1 more
TL;DR: The results from this study show that DE generally outperforms the other algorithms, however, on two noisy functions, both DE and PSO were outperformed by the EA.
Book ChapterDOI
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
Dervis Karaboga,Bahriye Basturk +1 more
TL;DR: The ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems to show superior performance on these kind of problems.
Proceedings ArticleDOI
Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation
TL;DR: A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean enables the evolution strategy to adapt the correct scaling of a given problem and also ensures invariance with respect to any rotation of the fitness function (or the coordinate system).