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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

Transparent and Efficient Parallelization of Swarm Algorithms

TL;DR: This article presents an approach for the efficient and transparent parallelization of a large class of swarm algorithms, specifically those where the multiagent paradigm is used to implement the functionalities of bioinspired entities, such as ants and birds.
Journal ArticleDOI

Kurtosis forecasting of bearing vibration signal based on the hybrid model of empirical mode decomposition and RVM with artificial bee colony algorithm

TL;DR: It is feasible for the proposed combination scheme to improve the prediction accuracy of RVM for kurtosis of bearing vibration signal and the RVM models with several different embedding dimensions and Gaussian RBF kernel parameters are used to compare with the EMD-ABCRVM method.
Journal ArticleDOI

Intelligent Multiple Search Strategy Cuckoo Algorithm for Numerical and Engineering Optimization Problems

TL;DR: The intelligent multiple search strategy algorithm (IMSS) is presented as a new modification of cuckoo search to improve performance of the conventional algorithm and employs Q-learning method to provide an intelligent balance between the exploration and exploitation behaviors.
Journal ArticleDOI

Robust possibilistic programming for joint order batching and picker routing problem in warehouse management

TL;DR: This study addresses joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model using genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms as solution approaches to solve the formulated model.
Proceedings ArticleDOI

PSO ingrained Artificial Bee Colony algorithm for solving continuous optimization problems

TL;DR: Simulation results and comparisons with the standard ABC and PSO show that the proposed variants can effectively enhance the searching efficiency and greatly improve the searching quality.
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.