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 power flow using artificial bee colony algorithm with global and local neighborhoods
TL;DR: The proposed approach describes the use of a modified artificial bee colony (ABC) algorithm called ABC with global and local neighborhoods (ABCGLN) to determine the optimal settings of OPF control variables to solve the OPF problem.
Journal ArticleDOI
Computational Experiment-Based Evaluation on Context-Aware O2O Service Recommendation
TL;DR: A computational experiment-based evaluation method of O2O service recommendation strategies, which mainly consists of three parts: customization of O 2O service strategies, modeling of experiment system, and execution of experiment evaluation, which shows that the proposed evaluation method is effective.
Journal ArticleDOI
A novel artificial bee colony algorithm based on modified search strategy and generalized opposition-based learning
TL;DR: The proposed algorithm can outperform ABC-based algorithms and other significant evolutionary optimizers in solving complex numerical optimization problems and modify dynamically frequency of perturbation at each iteration in order to enhance the global convergence.
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Swarm virus - Next-generation virus and antivirus paradigm?
TL;DR: The swarm virus prototype, designed here, mimics a swarm system behavior and thus follows the main idea underlying the swarm intelligence algorithms, which can be incorporated also to an antimalware systems, and can be analyzed for a future computer system protection.
Journal ArticleDOI
Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms
TL;DR: A comparison of several evolutionary (EAs) and bio-inspired (BIAs) algorithms, considered as global optimization methods, such as Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO) and Artificial Bee Colony on parameter estimation of crop growth SUCROS model.
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.