10,224 research outputs found
The Novel Approach of Adaptive Twin Probability for Genetic Algorithm
The performance of GA is measured and analyzed in terms of its performance
parameters against variations in its genetic operators and associated
parameters. Since last four decades huge numbers of researchers have been
working on the performance of GA and its enhancement. This earlier research
work on analyzing the performance of GA enforces the need to further
investigate the exploration and exploitation characteristics and observe its
impact on the behavior and overall performance of GA. This paper introduces the
novel approach of adaptive twin probability associated with the advanced twin
operator that enhances the performance of GA. The design of the advanced twin
operator is extrapolated from the twin offspring birth due to single ovulation
in natural genetic systems as mentioned in the earlier works. The twin
probability of this operator is adaptively varied based on the fitness of best
individual thereby relieving the GA user from statically defining its value.
This novel approach of adaptive twin probability is experimented and tested on
the standard benchmark optimization test functions. The experimental results
show the increased accuracy in terms of the best individual and reduced
convergence time.Comment: 7 pages, International Journal of Advanced Studies in Computer
Science and Engineering (IJASCSE), Volume 2, Special Issue 2, 201
Diffusion in a Granular Fluid - Simulation
The linear response description for impurity diffusion in a granular fluid
undergoing homogeneous cooling is developed in the preceeding paper. The
formally exact Einstein and Green-Kubo expressions for the self-diffusion
coefficient are evaluated there from an approximation to the velocity
autocorrelation function. These results are compared here to those from
molecular dynamics simulations over a wide range of density and inelasticity,
for the particular case of self-diffusion. It is found that the approximate
theory is in good agreement with simulation data up to moderate densities and
degrees of inelasticity. At higher density, the effects of inelasticity are
stronger, leading to a significant enhancement of the diffusion coefficient
over its value for elastic collisions. Possible explanations associated with an
unstable long wavelength shear mode are explored, including the effects of
strong fluctuations and mode coupling
Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm
n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de EconomĂa, Industria y Competitividad, project
number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad AutĂłnoma de Madrid, project
number S2013ICE-2933_02
Evolutionary optimization of optical antennas
The design of nano-antennas is so far mainly inspired by radio-frequency
technology. However, material properties and experimental settings need to be
reconsidered at optical frequencies, which entails the need for alternative
optimal antenna designs. Here a checkerboard-type, initially random array of
gold cubes is subjected to evolutionary optimization. To illustrate the power
of the approach we demonstrate that by optimizing the near-field intensity
enhancement the evolutionary algorithm finds a new antenna geometry,
essentially a split-ring/two-wire antenna hybrid which surpasses by far the
performance of a conventional gap antenna by shifting the n=1 split-ring
resonance into the optical regime.Comment: Also see Supplementary material, as attached to the main pape
A hybrid genetic algorithm for solving a layout problem in the fashion industry.
As of this writing, many success stories exist yet of powerful genetic algorithms (GAs) in the field of constraint optimisation. In this paper, a hybrid, intelligent genetic algorithm will be developed for solving a cutting layout problem in the Belgian fashion industry. In an initial section, an existing LP formulation of the cutting problem is briefly summarised and is used in further paragraphs as the core design of our GA. Through an initial attempt of rendering the algorithm as universal as possible, it was conceived a threefold genetic enhancement had to be carried out that reduces the size of the active solution space. The GA is therefore rebuilt using intelligent genetic operators, carrying out a local optimisation and applying a heuristic feasibility operator. Powerful computational results are achieved for a variety of problem cases that outperform any existing LP model yet developed.Fashion; Industry;
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