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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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A survey on the applications of artificial bee colony in signal, image, and video processing

TL;DR: The problems ABC algorithm has been applied in signal, image, and video processing fields and how ABC algorithm was used in the approaches for solving these kinds of problems are presented.
Journal ArticleDOI

Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos

TL;DR: In this article, the optimal placement and sizing of DG units play a major role in reducing system losses and in improving voltage profile and voltage stability, and the proposed algorithm is validated by testing it on a 38-node and 69-node radial distribution system.
Journal ArticleDOI

Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm

TL;DR: A novel hybrid algorithm that combines the artificial bee colony (ABC) and tabu search (TS) to solve the hybrid flow shop (HFS) scheduling problem with limited buffers to minimize the maximum completion time is presented.
Journal ArticleDOI

Design of limited-stop bus service with capacity constraint and stochastic travel time

TL;DR: A hybrid artificial bee colony (ABC) and Monte Carlo method is developed to solve the optimal stopping strategy of limited-stop bus service, which allows each bus vehicle to skip some stops.
Journal ArticleDOI

Artificial cooperative search algorithm for numerical optimization problems

TL;DR: The results obtained in the statistical analysis demonstrate that the success achieved by ACS algorithm in solving numerical optimization problems is better in comparison to the other computational intelligence algorithms used in this paper.
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.