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
Metastable Li1+δMn2O4 (0 ≤ δ ≤ 1) Spinel Phases Revealed by in Operando Neutron Diffraction and First-Principles Calculations
Bohang Song,Gabriel M. Veith,Jinseon Park,Mina Yoon,Pamela S. Whitfield,Melanie J. Kirkham,Jue Liu,Ashfia Huq +7 more
TL;DR: In this article, the authors examined the reaction mechanisms driven by the lithiation of Li1+δMn2O4 (0 ≤ δ ≤ 1) spinels via in operando neutron powder diffraction (NPD) and proposed new reaction mechanisms involving solid-solution regions within both cubic and tetragonal spinel phases.
Journal ArticleDOI
A hybrid fuzzy inference model based on RBFNN and artificial bee colony for predicting the uplift capacity of suction caissons
TL;DR: Comparisons show the intelligent fuzzy radial basis function neural network inference model (IFRIM) to be the fittest model for predicting the uplift capacity of suction caissons in terms of accuracy and reliability.
Journal ArticleDOI
A grey artificial bee colony algorithm
TL;DR: Inspired by differential evolution, a solution search equation called ABC/rand/2 is employed to balance the previous exploitation and a new perturbation scheme is also employed, which shows the effectiveness and superiority of GABC.
Journal ArticleDOI
Moth-flame optimization algorithm based on diversity and mutation strategy
TL;DR: An inertia weight of diversity feedback control is introduced in the moth-flame optimization to balance the algorithm’s exploitation and global search abilities and a small probability mutation after the position update stage is added to improve the optimization performance.
Journal ArticleDOI
An intelligent approach for variable size segmentation of non-stationary signals.
TL;DR: An enhanced segmentation method based on fractal dimension (FD) and evolutionary algorithms (EAs) for non-stationary signals, such as electroencephalogram (EEG), magnetoenceph analogram (MEG) and electromyogram (EMG), is proposed and is compared with other most popular approaches.
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.