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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
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 robust HDR images watermarking method using artificial bee colony algorithm

TL;DR: A robust watermarking method for High Dynamic Range Images (HDRIs) and the results have shown the superiority of the proposed algorithm to some state of the art methods.
Journal ArticleDOI

Investigations on Metaheuristic Algorithm for Designing Adaptive PID Controller for Continuous Stirred Tank Reactor

TL;DR: Design of the Proportional-Integral-Derivative (PID) controller using conventional and metaheuristic methods for the temperature control of the Continuous Stirred Tank Reactor is presented.
Journal Article

Optimized ready mixed concrete truck scheduling for uncertain factors using bee algorithm

TL;DR: The simulation results show that the BA approach can find the optimal solution better than GA in terms of efficiency and accuracy for all problems, and offers a more efficient alternative for solving RMC truck scheduling.
Journal ArticleDOI

Artificial bee colony directive for continuous optimization

TL;DR: This paper fundamentally modifies the solution search equations of the ABC in a manner that sends bee agents in search of three types of search regions that improve convergence speeds and proposes an innovative ABCD algorithm.
Journal ArticleDOI

Artificial bee colony optimization for modelling of indoor PLC channels: A case study from Turkey

TL;DR: Results show that the PLC channel model proposed in this study can be utilized to simulate PLC systems and to perform conformance tests of prototypes.
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.