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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.
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.

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Citations
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Journal ArticleDOI

Modified Differential Evolution Algorithm for Pattern Synthesis of Antenna Arrays

TL;DR: Experimental results demonstrate that the proposed MDE has better performance than the other optimization methods in both of these two examples, which indicate the proposed algorithm is a competitive optimization algorithm in pattern synthesis.
Journal ArticleDOI

ABC-GSPBFT: PBFT with grouping score mechanism and optimized consensus process for flight operation data-sharing

TL;DR: In this article , a Byzantine Fault-Tolerant (PBFT) with grouping score mechanism and artificial bee colony-optimized consensus process is designed to construct the consortium blockchain for sharing flight operation data.
Proceedings ArticleDOI

Identifying Optimum Artificial Bee Colony (ABC) Algorithm's Parameters for Scheduling the Manufacture and Assembly of Complex Products

TL;DR: The development of Artificial Bee Colony algorithm for solving the scheduling problem and the analysis of variance on the experimental results indicated that the performance can be improved dramatically after adopting the optimum parameter setting.
Journal ArticleDOI

Dynamic FDB selection method and its application: modeling and optimizing of directional overcurrent relays coordination

TL;DR: This article has four main objectives: to develop the dynamic fitness-distance balance (dFDB) selection method for meta-heuristic search algorithms, to develop a strong optimization algorithm using the dFDB method, to create an optimization model of the coordination of directional overcurrent relays (DOCRs) problem, and to optimize the DOCRs problem using the developed algorithm.
Journal ArticleDOI

A Survey on Optimization Algorithms for Optimizing the Numerical Functions

TL;DR: A comprehensive survey on numerical function optimization is presented below with a deeper view into different aspects of the algorithms.
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.