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
More filters
Journal ArticleDOI
Frequency Diverse Array Antenna Using Time-Modulated Optimized Frequency Offset to Obtain Time-Invariant Spatial Fine Focusing Beampattern
A-Min Yao,Wen Wu,Da-Gang Fang +2 more
TL;DR: In this paper, a combination of time-modulated and nonlinear distributed frequency offset is proposed to realize time-invariant spatial fine focusing beampatterns for a frequency diverse array (FDA) antenna.
Journal ArticleDOI
Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems
R. Rao,Vivek Patel +1 more
TL;DR: The proposed elitist TLBO algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms.
Journal ArticleDOI
Bacterial Colony Optimization
TL;DR: This paper investigates the behaviors at different developmental stages in Escherichia coli (E. coli) lifecycle and developing a new biologically inspired optimization algorithm named bacterial colony optimization (BCO), based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle.
Journal ArticleDOI
Artificial bee colony algorithm with memory
Xianneng Li,Guangfei Yang +1 more
TL;DR: A new ABC variant named ABC with memory algorithm (ABCM) is described, which imitates a memory mechanism to the artificial bees to memorize their previous successful experiences of foraging behavior and shows the superiority of ABCM on a set of benchmark problems.
Journal ArticleDOI
On the exploration and exploitation in popular swarm-based metaheuristic algorithms
TL;DR: The outcome of empirical study suggested that coherence and consistency in the swarm individuals throughout iterations is the key to success in swarm-based metaheuristics.
References
More filters
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.