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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

Online Evolutionary Swarm Algorithm for Self-Tuning Unmanned Flight Control Laws

TL;DR: In this paper, the inertial tensor of Bcm with respect to I, kg · m, expressed in B I, ki = I gain for proportional integral derivative P, kp = Kp gain for P, qe = quaternion error from B to I r = position, generic, m u = input to the plant v = velocity, generic m∕s v, v,v, v = velocities of B cm with respectto I, m ∕s, expressing in B x, y, z, z = position of
Proceedings ArticleDOI

A comparative study of African Buffalo Optimization and Randomized Insertion Algorithm for asymmetric Travelling Salesman's Problem

TL;DR: After attempting 15 benchmark ATSP cases out of the 19 datasets available in TSPLIB, it was discovered that the African Buffalo Optimization achieves slightly better result to the problems and at a much faster speed.
Journal ArticleDOI

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

TL;DR: In this article, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics and clustering of geological units in front of tunneling project.
Journal ArticleDOI

Low-Power FIR Filter Design Using Hybrid Artificial Bee Colony Algorithm with Experimental Validation Over FPGA

TL;DR: A novel EA, i.e., hybrid artificial bee colony algorithm, has been proposed and further applied for FIR filter design, which aims at satisfying the dual objectives of meeting the desired frequency domain specifications and power minimization.
Journal ArticleDOI

Exploration–exploitation balance in Artificial Bee Colony algorithm: a critical analysis

TL;DR: This study critically analyses the functional behaviour of ABC algorithm and finds that the scout bee operator may turn redundant while dealing with high dimensional problems and suggests that the ABC algorithm may be poor in exploration ability too for high-dimensional problems.
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.