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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An improved artificial bee colony algorithm combined with extremal optimization and Boltzmann Selection probability
TL;DR: The experimental results on three groups of benchmark functions indicate that the performance of the proposed algorithms is as good as or superior to those of 15 state-of-the-art optimization algorithms in terms of solution accuracy, convergence speed, successful rate and statistical tests.
Journal ArticleDOI
The Application of Artificial Bee Colony and Gravitational Search Algorithm in Reservoir Optimization
TL;DR: Results proved the superiority of the ABC compared to GSA with regards to faster convergence rate, stability, higher reliability and lower vulnerability indexes, while GSA is better in the resiliency indicator measure.
Proceedings ArticleDOI
An artificial bee colony algorithm for the cardinality-constrained portfolio optimization problems
TL;DR: This work investigates the trade-off between risk and return in a cardinality-constrained portfolio optimization problem and applied an artificial bee colony (ABC) method as the solution approach, which would be the first attempt of ABC on this application.
Journal ArticleDOI
Artificial bee colony optimized robust-reversible image watermarking
TL;DR: The embedding strength of watermarking is controlled with the help of artificial bee colony in order to get an optimal tradeoff between invisibility and robustness, and the proposed scheme is applied to a range of images to show its applicability to different domains.
Journal ArticleDOI
An efficient binary Gradient-based optimizer for feature selection.
TL;DR: The experimental results show that among the proposed binary GBO algorithms has the best comprehensive performance and has better performance than other well known metaheuristic algorithms in terms of the performance measures.
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.