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Journal ArticleDOI

A comparative study of Artificial Bee Colony algorithm

Dervis Karaboga, +1 more
- 01 Aug 2009 - 
- Vol. 214, Iss: 1, pp 108-132
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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.
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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.

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Proceedings ArticleDOI

Parallel artificial bee colony (PABC) algorithm

TL;DR: A parallel version of the artificial bee colony algorithm for shared memory architectures is presented and it is shown that the proposed parallelization strategy does not degrade the quality of solutions obtained, but achieves substantial speedup.
Journal ArticleDOI

A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem

TL;DR: In this paper, a hybrid artificial bee colony (HABC) algorithm is proposed for solving the fuzzy flexible job-shop scheduling problem (FFJSP), which uses multiple strategies in a combined way to generate the initial solutions with certain quality and diversity as the food sources, and applies the leftshift decoding scheme to convert solutions to active schedules.
Journal ArticleDOI

A novel binary artificial bee colony algorithm for the set-union knapsack problem

TL;DR: The results verify that the proposed approach is significantly superior to the baseline evolutionary algorithms for solving SUKP such as A-SUKP, ABC bin and binDE in terms of both time complexity and solution performance.
Journal ArticleDOI

Bare bones artificial bee colony algorithm with parameter adaptation and fitness-based neighborhood

TL;DR: A bare bones ABC with parameter adaptation and fitness-based neighborhood mechanism is proposed, BABC for short reference, and is able to significantly improve the performance of the original ABC and the modified ABC variants.
Journal ArticleDOI

Artificial bee colony algorithm with gene recombination for numerical function optimization

TL;DR: A novel gene recombination operator (GRO) is established into ABC by recombining the different superior genes of different good individuals for generating better offspring and could enhance the performance of ABC and ABC variants.
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

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.