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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Bidding Strategy for Virtual Power Plant Considering the Large-Scale Integrations of Electric Vehicles
TL;DR: In order to alleviate the energy crisis and environmental pollution, electric vehicles (EVs) have been developed on a large-scale throughout the world in the exceptional period and virtual power plant (VPP), an aggregator involved in various renewable energies and diversified loads, is considered as the crucial measure to coordinate with the EVs to participate in the power market.
Journal ArticleDOI
Balanced artificial bee colony algorithm
TL;DR: A new control parameter namely, cognitive learning factor CLF is introduced in the employed bees phase and onlooker bees phase, and the range of ABC control parameter φ is modified in order to find a trade-off between exploration and exploitation capability of ABC.
Journal ArticleDOI
An algebraic framework for swarm and evolutionary algorithms in combinatorial optimization
TL;DR: An algebraic framework is provided which allows to derive fully discrete variants of a large class of numerical evolutionary algorithms to tackle many combinatorial problems and shows that algebraic algorithms outperform the competitors and are competitive with the state-of-the-art results.
Journal ArticleDOI
Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm
TL;DR: The artificial bee colony algorithm proves itself to be most suited for this particular problem as it results in the minimum cost in the shortest time compared with those obtained from previously applied techniques.
Journal ArticleDOI
A new hybrid algorithm to solve bound-constrained nonlinear optimization problems
TL;DR: The goal of this work is to propose a hybrid algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions.
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.