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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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Journal ArticleDOI

Timetable Optimization for Regenerative Energy Utilization in Subway Systems

TL;DR: To improve RE utilization (REU) in a subway line, a timetable optimization problem is proposed and an improved artificial bee colony (IABC) algorithm is designed to solve it, which helps to improve the timetable currently used in this subway line.
Posted Content

Circle detection using electro-magnetism optimization

TL;DR: In this article, a circle detection method based on electromagnetism-like optimization (EMO) is proposed, which searches a solution based in the attraction and repulsion among prototype candidates.
Journal ArticleDOI

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

TL;DR: A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered, and a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem.
Journal ArticleDOI

A hybrid swarm intelligence based particle-bee algorithm for construction site layout optimization

TL;DR: The results show that the performance of PBA is comparable to those of the mentioned algorithms and can be efficiently employed to solve those hypothetical CSL problems with high dimensionality.
Posted Content

A Multi-threshold Segmentation Approach Based on Artificial Bee Colony Optimization

TL;DR: Experimental results over multiple images with different range of complexity validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness and demonstrate a better performance from the proposed algorithm.
References
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Proceedings ArticleDOI

Particle swarm optimization

TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book

Adaptation in natural and artificial systems

TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Book

Self-Organizing Maps

TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Book

Genetic Algorithms + Data Structures = Evolution Programs

TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.