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VINÍCIUS VELOSO DE MELO

Category Archives: Evolutionary algorithm

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Posted on by Posted in Differential evolution, Global Continuous Unconstrained Optimization, Journal, Metaheuristic, Promising region, Smart Sampling

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve

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Evaluating differential evolution with penalty function to solve constrained engineering problems

Posted on by Posted in Constrained Optimization, Differential evolution, Engineering Design, Journal, Metaheuristic

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the

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Phylogenetic Differential Evolution

Posted on by Posted in Differential evolution, Global Continuous Unconstrained Optimization, Journal, Machine learning, Metaheuristic

This paper presents a new technique for optimizing binary problems with building blocks. The authors have developed a different approach to existing Estimation of Distribution Algorithms (EDAs). Our technique, called Phylogenetic Differential Evolution (PhyDE), combines the Phylogenetic Algorithm and the Differential Evolution Algorithm. The first one is employed to identify the building blocks and to

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Benchmarking the multi-view differential evolution on the noiseless BBOB-2012 function testbed

Posted on by Posted in Conference, Differential evolution, Global Continuous Unconstrained Optimization, Metaheuristic, Multi-view, Self-adaptive

We propose a multi-view DE in which several mutation strategies are applied to the same current population to generate different views for the current iteration. The views are then merged using tournament-selection to generate the next single population. This Multi-View DE is benchmarked on the BBOB-2012 noiseless function testbed.     Keywords: procés de test.

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