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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Journal ArticleDOI
A hybrid approach to artificial bee colony algorithm
TL;DR: A hybrid approach based on the life cycle for the artificial bee colony algorithm to generate dynamical varying population as well as ensure appropriate balance between exploration and exploitation and the effectiveness of the proposed hybridization scheme is put forward.
Proceedings ArticleDOI
A novel chaotic artificial bee colony algorithm based on Tent map
TL;DR: The results demonstrate that, the STOC-ABC not only accelerates the convergence rate and improves solution precision, but also provides excellent performance in dealing with complex high-dimensional functions.
Journal ArticleDOI
The artificial bee colony algorithm in training artificial neural network for oil spill detection
TL;DR: A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).
Journal ArticleDOI
Optimal Synthesis of Linear Antenna Arrays Using Modified Spider Monkey Optimization
Urvinder Singh,Rohit Salgotra +1 more
TL;DR: Experimental results show that MSMO outperforms other popular algorithms like particle swarm optimization, cuckoo search, firefly algorithm, biogeography based optimization, differential evolution, tabu search and Taguchi method in terms of reduced side lobe level and faster convergence speed.
Journal ArticleDOI
Enhanced Moth-flame Optimization Based on Cultural Learning and Gaussian Mutation
TL;DR: An Enhanced Moth-Flame Optimization (EMFO) technique based on Cultural Learning (CL) and Gaussian Mutation (GM) is presented, which shows a significant improvement on MFO, in terms of solution quality and algorithmic reliability.
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.