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
Artificial bee colony with enhanced food locations for solving mechanical engineering design problems
Tarun Kumar Sharma,Ajith Abraham +1 more
TL;DR: The food locations in basic ABC are enhanced using Opposition based learning (OBL) concept and this variant is improved by incorporating greediness in searching behavior and named as I-ABC greedy, which helps in maintaining population diversity as well as enhance exploitation.
Journal ArticleDOI
Evaluation of gang saws’ performance in the carbonate rock cutting process using feasibility of intelligent approaches
TL;DR: In this paper, the authors used differential evolution (DE) and self-organizing map (SOM) algorithms to evaluate the electrical current consumed by the gang saw in the dimension stone cutting process.
Journal ArticleDOI
Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm
TL;DR: A comparative study with other population-based algorithms, like genetic algorithm, particle swarm optimization, and ant colony optimization algorithm, proves the global applicability and acceptability of ABC algorithm for parametric optimization.
Proceedings ArticleDOI
A discrete artificial bee colony algorithm for the permutation flow shop scheduling problem with total flowtime criterion
TL;DR: A discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures is presented.
Journal ArticleDOI
COOA: Competitive optimization algorithm
TL;DR: A novel optimization algorithm based on competitive behavior of various creatures such as birds, cats, bees and ants to survive in nature and is an efficient method in finding the solution of optimization 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.