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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Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
TL;DR: In this new "genetic programming" paradigm, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (recombination) operator appropriate for genetically mating computer programs.
Book ChapterDOI
Fast Evolution Strategies
TL;DR: It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper.
Journal ArticleDOI
A new design method based on artificial bee colony algorithm for digital IIR filters
TL;DR: A new method based on ABC algorithm for designing digital IIR filters is described and its performance is compared with that of a conventional optimization algorithm (LSQ-nonlin) and particle swarm optimization (PSO) algorithm.
Book
Self-Organizing Maps
TL;DR: The mathematical preliminaries, background, basic ideas, and implications are expounded in a clear, well-organized form, accessible without prior expert knowledge, and the contents are handled with theoretical rigor.