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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Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines
Min-Yuan Cheng,Minh-Tu Cao +1 more
TL;DR: A 10-fold cross-validation approach found EMARS to be the best model for predicting CL and HL with 65% and 45% deduction in terms of RMSE, respectively, compared to other methods.
Journal ArticleDOI
An Effective Artificial Bee Colony Algorithm for a Real-World Hybrid Flowshop Problem in Steelmaking Process
TL;DR: An effective artificial bee colony (ABC) algorithm with the job-permutation-based representation for solving the scheduling problem resulting from a steelmaking process is proposed.
Book
Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
TL;DR: This paper presents a meta-anatomy of evolutionary algorithms and some examples of successful and unsuccessful attempts at optimization in the context of discrete-time programming.
Journal ArticleDOI
PSO-based analysis of Echo State Network parameters for time series forecasting
TL;DR: Throughout this paper, a PSO algorithm is associated to ESN to pre-train some fixed weights values within the network to optimize the untrained weights.
Journal ArticleDOI
A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model
TL;DR: It can be concluded that the DE and ACO algorithms are considerably more adaptive in optimizing the forecasting problem for the HNN model, which is based on fuzzy pattern-recognition and continuity equation.
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.