scholarly journals Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3329
Author(s):  
Sergey Salihov ◽  
Dmitriy Maltsov ◽  
Maria Samsonova ◽  
Konstantin Kozlov
Keyword(s):  
Differential Evolution ◽  
Genomic Selection ◽  
Optimization Problems ◽  
Snp Markers ◽  
Selection Model ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Wide Range ◽  
Genomic Selection Model

The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.

scholarly journals About stability of mixed integer optimization problems under perturbations of input data of vector criterion

2021 ◽  
pp. 7-11
Author(s):  
Tetiana Lebedeva ◽  
Natalia Semenova ◽  
Tetiana Sergienko
Keyword(s):  
Initial Data ◽  
Input Data ◽  
Optimization Problems ◽  
Point Of View ◽  
Mixed Integer ◽  
Stability Of Solutions ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Small Perturbations ◽  
Vector Criterion

The article is devoted to the study of the influence of uncertainty in initial data on the solutions of mixed integer optimization vector problems. In the optimization problems, including problems with vector criterion, small perturbations in initial data can result in solutions strongly different from the true ones. The problem of stability of the indicated tasks is studied from the point of view of direct coupled with her question in relation to stability of solutions belonging to some subsets of feasible set.

Introduction

1995 ◽  
Author(s):  
Christodoulos A. Floudas
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
Optimization Problems ◽  
Batch Process ◽  
Mixed Integer ◽  
Optimization Models ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Location Allocation ◽  
Primary Focus

This chapter introduces the reader to elementary concepts of modeling, generic formulations for nonlinear and mixed integer optimization models, and provides some illustrative applications. Section 1.1 presents the definition and key elements of mathematical models and discusses the characteristics of optimization models. Section 1.2 outlines the mathematical structure of nonlinear and mixed integer optimization problems which represent the primary focus in this book. Section 1.3 illustrates applications of nonlinear and mixed integer optimization that arise in chemical process design of separation systems, batch process operations, and facility location/allocation problems of operations research. Finally, section 1.4 provides an outline of the three main parts of this book. A plethora of applications in all areas of science and engineering employ mathematical models. A mathematical model of a system is a set of mathematical relationships (e.g., equalities, inequalities, logical conditions) which represent an abstraction of the real world system under consideration. Mathematical models can be developed using (i) fundamental approaches, (ii) empirical methods, and (iii) methods based on analogy. In (i), accepted theories of sciences are used to derive the equations (e.g., Newton’s Law). In (ii), input-output data are employed in tandem with statistical analysis principles so as to generate empirical or “black box” models. In (iii), analogy is employed in determining the essential features of the system of interest by studying a similar, well understood system. The variables can take different values and their specifications define different states of the system. They can be continuous, integer, or a mixed set of continuous and integer. The parameters are fixed to one or multiple specific values, and each fixation defines a different model. The constants are fixed quantities by the model statement. The mathematical model relations can be classified as equalities, inequalities, and logical conditions. The model equalities are usually composed of mass balances, energy balances, equilibrium relations, physical property calculations, and engineering design relations which describe the physical phenomena of the system. The model inequalities often consist of allowable operating regimes, specifications on qualities, feasibility of heat and mass transfer, performance requirements, and bounds on availabilities and demands. The logical conditions provide the connection between the continuous and integer variables.

A feasible rounding approach for mixed-integer optimization problems

2018 ◽  
Vol 72 (2) ◽  
pp. 309-337 ◽  
Author(s):  
Christoph Neumann ◽  
Oliver Stein ◽  
Nathan Sudermann-Merx
Keyword(s):  
Optimization Problems ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Mixed Integer Optimization

An Evolutionary Lagrange Method for Mechanical Design

2010 ◽  
Vol 44-47 ◽  
pp. 1817-1822
Author(s):  
Yung Chin Lin ◽  
Yung Chien Lin ◽  
Kun Song Huang ◽  
Kuo Lan Su
Keyword(s):  
Design Problem ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Inequality Constraints ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Constrained Optimization Problems ◽  
Lagrange Method ◽  
Mechanical Design Problem

A novel application to mechanical optimal design is presented in this paper. Here, an evolutionary algorithm, called mixed-integer differential evolution (MIHDE), is used to solve general mixed-integer optimization problems. However, most of real-world mixed-integer optimization problems frequently consist of equality and/or inequality constraints. In order to effectively handle constraints, an evolutionary Lagrange method based on MIHDE is implemented to solve the mixed-integer constrained optimization problems. Finally, the evolutionary Lagrange method is applied to a mechanical design problem. The satisfactory results are achieved, and demonstrate that the evolutionary Lagrange method can effectively solve the optimal mechanical design problem.

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