Journal ArticleDOI
A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga,Bahriye Akay +1 more
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.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
Citations
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Journal ArticleDOI
A comprehensive survey on optimizing deep learning models by metaheuristics
TL;DR: This survey formulate the optimization problems in DNN design such as architecture optimization, hyper-parameter optimization, training and feature representation level optimization, and the encoding schemes used in metaheuristics to represent the network architectures are categorized.
Journal ArticleDOI
Discrete wavelet transform-based color image watermarking using uncorrelated color space and artificial bee colony
TL;DR: A novel discrete wavelet transform (DWT) based color image watermarking method is proposed which embeds the color watermark into host image using uncorrelated color space (UCS) and artificial bee colony (ABC) method and results show that proposed method outperforms other existing methods against the various signal processing attacks.
Journal ArticleDOI
Two modified versions of artificial bee colony algorithm
TL;DR: Two aspects of the ABC algorithm are modified and new configurations are used and results show that the new changes have positive effects on the performance of ABC algorithm.
Journal ArticleDOI
A mechanism based on Artificial Bee Colony to generate diversity in Particle Swarm Optimization
TL;DR: A mechanism based on the ABC to generate diversity when all particles of the PSO converge to a single point of the search space is put forward, which was evaluated and compared to other well known swarm based approaches in all benchmark functions recently proposed in CEC 2010 for large scale optimization.
Journal ArticleDOI
Structural damage identification based on modified Artificial Bee Colony algorithm using modal data
TL;DR: Estimated results show that the present technique can produce more accurate damage identification results, comparing with other evolutionary algorithms, even with a few noise contaminated measurements.
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
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.