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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Proceedings ArticleDOI
Application of Artificial Bee Colony to economic load dispatch problem with ramp rate limits and prohibited operating zones
TL;DR: The proposed methodology was found to be robust, fast converging and more proficient over other existing techniques.
Journal ArticleDOI
Ensemble Selection based on Classifier Prediction Confidence
TL;DR: This paper proposes an ensemble selection method that takes into account each base classifier's confidence during classification and the overall credibility of the base classifiers in the ensemble and achieves much better performance in comparison to some ensemble methods.
Journal ArticleDOI
Adaptive Configuration of evolutionary algorithms for constrained optimization
TL;DR: This paper proposes a new algorithm framework that uses multiple methodologies, where each methodology uses multiple search operators, and introduces it as the EA with Adaptive Configuration, where the first level is to decide the methodologies and the second level isto decide the search operators.
Journal ArticleDOI
Future search algorithm for optimization
TL;DR: A better performance of the proposed algorithm to get the optimal solution with fewer iterations number than other methods is confirmed.
Proceedings ArticleDOI
Bee colony algorithm for real-time optimal path planning of mobile robots
TL;DR: This paper presents a novel method to solve the problem of path planning for mobile robots based on bee colony algorithm, inspired by collective behavior of honeybees to find food sources around the hive.
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.