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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On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology
TL;DR: The typical results prove ABC to excel PSO after relatively short optimization cycles, indicating the great premise of ABC methodology to be used for well-optimization purposes.
Journal ArticleDOI
A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification
Yugal Kumar,Gadadhar Sahoo +1 more
TL;DR: The compendious survey on the GSA algorithm and its applications is presented as well as enlightens the applicability of GSA in data clustering & classification.
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A two-step artificial bee colony algorithm for clustering
Yugal Kumar,Gadadhar Sahoo +1 more
TL;DR: Both the experimental and statistical analyses show that improvements in ABC algorithm have an advantage over the conventional ABC algorithm for solving clustering problems.
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A hybrid state-of-charge estimation method based on credible increment for electric vehicle applications with large sensor and model errors
TL;DR: A novel SOC estimation method based on the SOC increment with high credibility is proposed to deal with the large errors of the model and sensor and can greatly suppress the large sensor and model errors and achieve high accuracy and robustness.
Journal ArticleDOI
A new meta-heuristic butterfly-inspired algorithm
Xiangbo Qi,Yunlong Zhu,Hao Zhang +2 more
TL;DR: The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions, and it proves that the ABO algorithm provides a new effective computational framework for solving optimization problems.
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.