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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Capacitor placement for net saving maximization and system stability enhancement in distribution networks using artificial bee colony-based approach
TL;DR: In this paper, the authors proposed an approach to allocate static capacitors along radial distribution networks using the artificial bee colony algorithm, where the algorithm identifies optimal sizing and placement and takes the final decision for optimum location within the number of buses nominated.
Journal ArticleDOI
Opposition based lévy flight artificial bee colony
TL;DR: The proposed algorithm is named as Opposition Based Lévy Flight ABC, which outperforms the basic ABC and its recent variants namely Gbest guided ABC, Best-So-Far ABC, and Modified ABC in most of the experiments.
Journal ArticleDOI
The improvement of glowworm swarm optimization for continuous optimization problems
TL;DR: Numerical results reveal that the proposed algorithms can find better solutions when compared to classical GSO and other heuristic algorithms and are powerful search algorithms for various global optimization problems.
Journal ArticleDOI
A new Kho-Kho optimization Algorithm: An application to solve combined emission economic dispatch and combined heat and power economic dispatch problem
TL;DR: The proposed technique is a population based meta-heuristic method which is inspired from the strategies used by players in a well known tag-team game played in India, i.e. Kho-Kho, and its performance and superiority with respect to other existing methods are evaluated.
Journal ArticleDOI
Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms
TL;DR: WDE’s success in solving CEC’ 2013 problems was compared to 4 different EAs statistically and results showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.
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.