最优多元混合的遗传算法

IF 3.6 1区数学 Q1 MATHEMATICS, APPLIED

Mathematical Models & Methods in Applied Sciences Pub Date : 2023-01-01 DOI:10.12988/ams.2023.917307

Giacinto Angelo Sgarro, L. Grilli

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引用次数: 0

摘要

本文提出了一种算法，从一组由一组变量(特征)描述的一组元素(项)开始，寻找尽可能接近理想解的最优混合物。这类优化问题可以通过属于运筹学(OR)领域的传统方法解决，甚至可以通过属于人工智能(AI)领域的元启发式技术来解决。为了呈现人工智能的视角，本文采用遗传算法(GA)模型，并通过与线性规划(LP)求解器在一组8项5特征实验上的比较，证明了其一致性。结果表明，所提出的遗传算法收敛于全局最优，并具有竞争性

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Genetic algorithm for optimal multivariate mixture

This paper proposes an algorithm to ﬁnd an optimal mixture that is as close as possible to an ideal solution, starting from a set of elements (items) described by a set of variables (features). This class of optimization problems can be tackled through traditional approaches belonging to the ﬁeld of operations research (OR) or even through meta-heuristics techniques belonging to the ﬁeld of artiﬁcial intelligence (AI). In order to present an artiﬁcial intelligence perspective, this paper uses a genetic algorithm (GA) model which proves its consistency through the comparison with a linear programming (LP) solver on a set of 8-items 5-features experiments. Results show that the proposed GA converges towards the global optimum and provides competitive results

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来源期刊

Mathematical Models & Methods in Applied Sciences 数学-应用数学

CiteScore

6.30

自引率

17.10%

发文量

审稿时长

1 months

期刊介绍： The purpose of this journal is to provide a medium of exchange for scientists engaged in applied sciences (physics, mathematical physics, natural, and technological sciences) where there exists a non-trivial interplay between mathematics, mathematical modelling of real systems and mathematical and computer methods oriented towards the qualitative and quantitative analysis of real physical systems. The principal areas of interest of this journal are the following: 1.Mathematical modelling of systems in applied sciences; 2.Mathematical methods for the qualitative and quantitative analysis of models of mathematical physics and technological sciences; 3.Numerical and computer treatment of mathematical models or real systems. Special attention will be paid to the analysis of nonlinearities and stochastic aspects. Within the above limitation, scientists in all fields which employ mathematics are encouraged to submit research and review papers to the journal. Both theoretical and applied papers will be considered for publication. High quality, novelty of the content and potential for the applications to modern problems in applied sciences and technology will be the guidelines for the selection of papers to be published in the journal. This journal publishes only articles with original and innovative contents. Book reviews, announcements and tutorial articles will be featured occasionally.