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Journal ArticleDOI

A comparative study of Artificial Bee Colony algorithm

Dervis Karaboga, +1 more
- 01 Aug 2009 - 
- Vol. 214, Iss: 1, pp 108-132
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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.
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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.

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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

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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Journal ArticleDOI

Transport modeling by multi-agent systems: a swarm intelligence approach

TL;DR: This article tries to obtain the answer to the following question: Can some principles of natural swarm intelligence in the development of artificial systems aimed at solving complex problems in traffic and transportation?
Book ChapterDOI

Reaction-Diffusion Model of a Honeybee Colony's Foraging Behaviour

TL;DR: The results elucidate the role of natural clustering of the dances in the small area of the have - it has to facilitate the information flow that is beneficial for overall process of colony's food collection.
Proceedings ArticleDOI

Design of fuzzy logic controllers using genetic algorithms

TL;DR: From the simulation results, it is found that the designed FLC is robust and can drive the cart system from any given initial state to the desired final state, which verifies the feasibility and validity of the proposed method.
Journal ArticleDOI

How Information-Mapping Patterns Determine Foraging Behaviour of a Honey Bee Colony

TL;DR: A model of foraging behaviour of a honeybee colony based on reaction-diffusion equations is developed and studied how mapping the information about the explored environment to the hive determines this behaviour.
Book ChapterDOI

Using bees to solve a data-mining problem expressed as a max-sat one

TL;DR: This paper presents a very recent metaheuristic introduced to solve a 3-sat problem, based on the process of bees' reproduction, which can be classified as an evolutionary algorithm.