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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Proceedings ArticleDOI
Overview of Artificial Bee Colony (ABC) algorithm and its applications
TL;DR: An overview of the literature employing the ABC algorithm, a recently introduced population-based meta-heuristic optimization technique inspired by the intelligent foraging behavior of honeybee swarms, is presented.
Journal ArticleDOI
Velocity based artificial bee colony algorithm for high dimensional continuous optimization problems
TL;DR: This work proposes a hybrid ABC algorithm so called VABC, which improves the ABC's exploitation strategy by applying a new search equation in the onlooker phase, which uses the PSO search strategy to guide the search for candidate solutions.
Journal ArticleDOI
Ideology algorithm: a socio-inspired optimization methodology
Teo Ting Huan,Anand J. Kulkarni,Anand J. Kulkarni,Jeevan Kanesan,Chuah Joon Huang,Ajith Abraham +5 more
TL;DR: The results from this study highlighted that the IA outperforms the other algorithms in terms of number function evaluations and computational time.
Journal ArticleDOI
Spam filtering using a logistic regression model trained by an artificial bee colony algorithm
TL;DR: A novel spam detection method that combines the artificial bee colony algorithm with a logistic regression classification model is proposed that outperforms other spam detection techniques considered in this study in terms of classification accuracy.
Journal ArticleDOI
Hybrid multiple objective artificial bee colony with differential evolution for the time–cost–quality tradeoff problem
TL;DR: The MOABCDE-TCQT, a new hybrid multiple objective evolutionary algorithm that is based on hybridization of artificial bee colony and differential evolution, is proposed to solve time–cost–quality tradeoff problems.
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.