2,270 research outputs found
Metaheuristic Algorithms for Convolution Neural Network
A typical modern optimization technique is usually either heuristic or
metaheuristic. This technique has managed to solve some optimization problems
in the research area of science, engineering, and industry. However,
implementation strategy of metaheuristic for accuracy improvement on
convolution neural networks (CNN), a famous deep learning method, is still
rarely investigated. Deep learning relates to a type of machine learning
technique, where its aim is to move closer to the goal of artificial
intelligence of creating a machine that could successfully perform any
intellectual tasks that can be carried out by a human. In this paper, we
propose the implementation strategy of three popular metaheuristic approaches,
that is, simulated annealing, differential evolution, and harmony search, to
optimize CNN. The performances of these metaheuristic methods in optimizing CNN
on classifying MNIST and CIFAR dataset were evaluated and compared.
Furthermore, the proposed methods are also compared with the original CNN.
Although the proposed methods show an increase in the computation time, their
accuracy has also been improved (up to 7.14 percent).Comment: Article ID 1537325, 13 pages. Received 29 January 2016; Revised 15
April 2016; Accepted 10 May 2016. Academic Editor: Martin Hagan. in Hindawi
Publishing. Computational Intelligence and Neuroscience Volume 2016 (2016
Review of Metaheuristics and Generalized Evolutionary Walk Algorithm
Metaheuristic algorithms are often nature-inspired, and they are becoming
very powerful in solving global optimization problems. More than a dozen of
major metaheuristic algorithms have been developed over the last three decades,
and there exist even more variants and hybrid of metaheuristics. This paper
intends to provide an overview of nature-inspired metaheuristic algorithms,
from a brief history to their applications. We try to analyze the main
components of these algorithms and how and why they works. Then, we intend to
provide a unified view of metaheuristics by proposing a generalized
evolutionary walk algorithm (GEWA). Finally, we discuss some of the important
open questions.Comment: 14 page
An improved optimization technique for estimation of solar photovoltaic parameters
The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV
Using 2-Opt based evolution strategy for travelling salesman problem
Harmony search algorithm that matches the (µ+ 1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions
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