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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Artificial bee colony algorithm for solving multi-objective optimal power flow problem
M. Rezaei Adaryani,A. Karami +1 more
TL;DR: In this paper, an Artificial Bee Colony (ABC) algorithm is employed as the main optimizer for optimal adjustments of the power system control variables of the OPF problem, which involves both continuous and discrete variables.
Journal ArticleDOI
A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops
TL;DR: A discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime is presented.
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Global optimization of clusters of rigid molecules using the artificial bee colony algorithm
Jun Zhang,Michael Dolg +1 more
TL;DR: ABCluster was extended to the optimization of clusters of rigid molecules, where "rigid" means that all internal degrees of freedom of the constituent molecules are frozen.
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Red deer algorithm (RDA): a new nature-inspired meta-heuristic
Amir Mohammad Fathollahi-Fard,Mostafa Hajiaghaei-Keshteli,Reza Tavakkoli-Moghaddam,Reza Tavakkoli-Moghaddam +3 more
TL;DR: The main inspiration of this meta- heuristic algorithm is to originate from an unusual mating behavior of Scottish red deer in a breading season, and the superiority of the proposed RDA shows in comparison with other well-known and recent meta-heuristics.
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Evolutionary techniques in optimizing machining parameters
TL;DR: An overview and the comparison of the latest five year researches from 2007 to 2011 that used evolutionary optimization techniques to optimize machining process parameter of both traditional and modern machining are given.
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.