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
A hybrid ABC-SPSO algorithm for continuous function optimization
TL;DR: This paper investigates the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO).
Journal ArticleDOI
Artificial bee colony algorithm for CONWIP production control system in a multi-product multi-machine manufacturing environment
Saeede Ajorlou,Issac Shams +1 more
TL;DR: A mathematical model for the system is presented, and then an artificial bee colony optimization algorithm is applied to simultaneously find the optimal work-in-process inventory level as well as job sequence order in order to minimize the overall makespan time.
Journal ArticleDOI
Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection
TL;DR: This work uses a clustering technique in order to make the initial population distributed over the entire feature space and to increase the inclusion of features which are more promising in the proposed model.
Journal ArticleDOI
Combining Artificial Bee Colony With Ordinal Optimization for Stochastic Economic Lot Scheduling Problem
TL;DR: An algorithm combining artificial bee colony (ABC) approach and ordinal optimization (OO) theory, abbreviated as ABCOO, is proposed to find a good enough base-stock level of the FSBS system using reasonable computation time and is used to solve an SELSP involving 12 products and three queuing models.
Proceedings ArticleDOI
A new approach for template matching in digital images using an Artificial Bee Colony Algorithm
TL;DR: The artificial bee colony algorithm (ABC) is applied to the object recognition in the images, showing that the ABC is faster than other evolutionary algorithms and with comparable accuracy.
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.