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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An Improved Artificial Bee Colony algorithm for real-world hybrid flowshop rescheduling in Steelmaking-refining-Continuous Casting process
TL;DR: In the Improved Artificial Bee Colony (IABC), novel encoding and decoding strategies are devised to represent the solutions effectively, where a charge left-shifting strategy is designed to decrease cast break and a worst solution replacement strategy is developed to further enhance the exploitation ability.
Journal ArticleDOI
Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization
TL;DR: In this article, an enhanced bee colony optimization (EBCO) is proposed to solve the daily economic dispatch of MG systems, where the self-adaption repulsion factor is embedded in the bee swarm of the BCO in order to improve the behavior patterns of each bee swarm and increase its search efficiency and accuracy in high dimensions.
Journal ArticleDOI
Construction of Equivalent Circuit of a Single and Isolated Transformer Winding From FRA Data Using the ABC Algorithm
Pritam Mukherjee,L. Satish +1 more
TL;DR: In this paper, an artificial bee colony search algorithm was employed for synthesizing a mutually coupled lumped-parameter ladder-network representation of a transformer winding, starting from its measured magnitude frequency response.
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Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems
Ali Ebrahimi,Ehsan Khamehchi +1 more
TL;DR: The results and comparison of its performance with other algorithms show that SWA's performance is superior to other algorithms and it could be confidently used in optimization tasks.
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.