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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Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm
TL;DR: In this paper, a backtracking search optimization algorithm (BSOA) is addressed to assign the distributed generators (DGs) along radial distribution networks, which is adapted with weighting factor to reduce the network real loss and enhance the voltage profile with the purpose of improving the operating performance.
Journal ArticleDOI
A novel particle swarm optimization algorithm with Levy flight
Huseyin Hakli,Harun Uğuz +1 more
TL;DR: Experimental results show that the LFPSO is clearly seen to be more successful than one of the state-of-the-art PSO (SPSO) and the other PSO variants in terms of solution quality and robustness and compared with well-known and recent population-based optimization methods.
Journal ArticleDOI
Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur's, Otsu and Tsallis functions
TL;DR: Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.
Journal ArticleDOI
A new metaheuristic for numerical function optimization: Vortex Search algorithm
Berat Doğan,Tamer Ölmez +1 more
TL;DR: The results indicate that the proposed Vortex Search algorithm outperforms the SA, PS and ABC algorithms while being competitive with the PSO2011 algorithm.
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Modified Grey Wolf Optimizer for Global Engineering Optimization
TL;DR: Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.
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.