Special Issue "Numerical Optimization and Applications"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 30 June 2020.

Special Issue Editor

Prof. Hsien-Chung Wu
Website
Guest Editor
Department of Mathematics, National Kaohsiung Normal University, Kaohsiung 802, Taiwan
Interests: : fuzzy optimization; fuzzy real analysis; fuzzy statistical analysis; operations research; computational intelligence; soft computing; fixed point theory; applied functional analysis
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Special Issue Information

Dear Colleagues,

Numerical optimization is a branch of mathematical and numerical analysis. Because of the growing use of optimization in practical problems, almost every problem in science, engineering, and economics can be formulated as an optimization problem in which the computational techniques based on mathematical analysis are frequently designed to solve those problems. Studying the approximate solutions of optimization problems with the presence of computational errors and the convergent behavior of algorithms are the important issues. However, some of the problems are very difficult to solve using the approaches of mathematical analysis. In this case, natural computing is an important tool to solve the hard optimization problems for the purpose of generating the near-optimal solutions, where natural computing is concerned with computing inspired by nature. The topics of this Special Issue include, but are not limited to:

  • Variants of Newton methods
  • Interior-point method
  • Conjugate gradient method
  • Proximal point method
  • Predictor–corrector method
  • Trust-region method
  • Simulation-based optimization

Natural computing (evolutionary computation, neural computation, quantum computation, ant colony optimization, artificial immune systems, swarm intelligence, etc.)

Prof. Dr. Hsien-Chung Wu
Guest Editor

Manuscript Submission Information

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Keywords

  • Convergence analysis
  • Genetic algorithms
  • Gradient and subgradient
  • Metaheuristics
  • Nonsmooth optimization
  • Nondifferentiability and subdifferentiability
  • Near-optimal solution
  • Simulated annealing

Published Papers (2 papers)

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Research

Open AccessArticle
An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis
Mathematics 2019, 7(11), 1051; https://doi.org/10.3390/math7111051 - 04 Nov 2019
Cited by 3
Abstract
This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel [...] Read more.
This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions. Full article
(This article belongs to the Special Issue Numerical Optimization and Applications)
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Open AccessArticle
Numerical Method for Solving the Robust Continuous-Time Linear Programming Problems
Mathematics 2019, 7(5), 435; https://doi.org/10.3390/math7050435 - 16 May 2019
Abstract
A robust continuous-time linear programming problem is formulated and solved numerically in this paper. The data occurring in the continuous-time linear programming problem are assumed to be uncertain. In this paper, the uncertainty is treated by following the concept of robust optimization, which [...] Read more.
A robust continuous-time linear programming problem is formulated and solved numerically in this paper. The data occurring in the continuous-time linear programming problem are assumed to be uncertain. In this paper, the uncertainty is treated by following the concept of robust optimization, which has been extensively studied recently. We introduce the robust counterpart of the continuous-time linear programming problem. In order to solve this robust counterpart, a discretization problem is formulated and solved to obtain the ϵ -optimal solution. The important contribution of this paper is to locate the error bound between the optimal solution and ϵ -optimal solution. Full article
(This article belongs to the Special Issue Numerical Optimization and Applications)
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