Journal ArticleDOI
A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
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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.About:
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.read more
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Unmanned Aerial Vehicle Base Station (UAV-BS) Deployment With Millimeter-Wave Beamforming
TL;DR: This article considers millimeter-wave (mmWave) communication on a UAV platform, where the UAV base station (UAV-BS) serves multiple ground users, which generate big sensor data.
Journal ArticleDOI
Enhancing artificial bee colony algorithm using more information-based search equations
TL;DR: A novel ABC method called EABC is proposed, which significantly improves the performance of ABC, offering faster global convergence, higher solution quality, and stronger robustness when compared with the other algorithms.
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An image watermarking scheme in wavelet domain with optimized compensation of singular value decomposition via artificial bee colony
TL;DR: Experimental results demonstrated that the proposed image watermarking scheme developed in the wavelet domain possesses the strong robustness against image manipulation attacks, but also, is comparable to other schemes in term of visual quality.
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A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems
TL;DR: The experimental results show that the HPA algorithm is an alternative and competitive optimizer for continuous optimization problems.
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Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures
TL;DR: The results indicate that the artificial neural network, the weights of which had been optimized via the ABC algorithm, exhibits greater ability, flexibility and accuracy in comparison with statistical models.
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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
Rainer Storn,Kenneth Price +1 more
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.