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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Journal ArticleDOI
MBA-IF:A New Data Clustering Method Using Modified Bat Algorithm and Levy Flight
R. Jensi,G. Wiselin Jiji +1 more
TL;DR: A new data clustering method using modified bat algorithm is presented and the experimental results show that the proposed algorithm is suitable forData clustering in an efficient and robust way.
Journal ArticleDOI
Comparison study for clonal selection algorithm and genetic algorithm
Ezgi Deniz Ulker,Sadik Ulker +1 more
TL;DR: This work examines the comparative performances of two algorithms, a special selection algorithm, Clonal Selection Algorithm (CLONALG), which is a subset of Artificial Immune Systems, and Genetic Algorithms, tested with certain benchmark functions.
Proceedings ArticleDOI
Online Synchronization of Uncertain Chaotic Systems by Artificial Bee Colony Algorithm in a Non-Lyapunov Way
TL;DR: An artificial bee colony (ABC) algorithm approach is newly introduced to solve this problem in a non-Lyapunov way, and the classical Henon systems with different initials are used in respectively, in the conditions that the systematic parameters are different and uncertain.
Proceedings ArticleDOI
An improved artificial bee colony algorithm
Xiaojun Bi,Yanjiao Wang +1 more
TL;DR: In this article, a fast mutation artificial bee colony algorithm or FMABC was proposed. But, the mutation strategy based on opposition-based learning was proposed instead of the behavior of scouts.
Book ChapterDOI
Hybrid Guided Artificial Bee Colony Algorithm for Numerical Function Optimization
TL;DR: GABC and GGABC methods are hybrid and so-called Hybrid Guided Artificial Bee Colony (HGABC) algorithm for strong discovery and utilization processes and experiment results show that the proposed HGABC algorithm outperforms ABC, PSO, GABC andGGABC algorithms in most of the experiments.
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.