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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
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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Citations
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An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time

TL;DR: IABC algorithm can solve FJSP with fuzzy processing time effectively, both benchmark cases and real-life remanufacturing instances, and can be as part of expert and intelligent scheduling system to supply decision support for reManufacturing scheduling and management.
Journal ArticleDOI

A novel bee swarm optimization algorithm for numerical function optimization

TL;DR: The experimental results show that the BSO algorithms are effective and robust; produce excellent results, and outperform other algorithms investigated in this consideration.
Journal ArticleDOI

Water strider algorithm: A new metaheuristic and applications

TL;DR: The Water Strider Algorithm is a population-based optimizer inspired by the life cycle of water strider bugs that is applied to classical constrained, unconstrained, continuous and discrete structural design problems confirming its capability of tackling various challenging problems.
Journal ArticleDOI

A swarm intelligence approach to the quadratic minimum spanning tree problem

TL;DR: An artificial bee colony (ABC) algorithm is presented, a new swarm intelligence approach inspired by intelligent foraging behavior of honey bees, to solve the quadratic minimum spanning tree problem.
Journal ArticleDOI

Discrete Spider Monkey Optimization for Travelling Salesman Problem

TL;DR: An effective variant of SMO to solve TSP called discrete SMO (DSMO), where every spider monkey represents a TSP solution where Swap Sequence and Swap Operator based operations are employed, which enables interaction among monkeys in obtaining the optimal T SP solution.
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.