Evolutionary Optimization on Problems Subject to Changes of Variables

2010 ◽  
pp. 151-160 ◽  
Author(s):  
Richard Allmendinger ◽  
Joshua Knowles
Keyword(s):  
Evolutionary Optimization

Genetic algorithm application for optimization of geometry of synchronous electric motor with permanent magnets by criterion of electromagnetic torque

2019 ◽  
pp. 56-62
Author(s):  
I. N. Belezyakov ◽  
K. G. Arakancev
Keyword(s):  
Genetic Algorithm ◽  
Electric Motor ◽  
Permanent Magnets ◽  
Evolutionary Optimization ◽  
Electric Machines ◽  
Geometric Parameters ◽  
Geometrical Parameters ◽  
Electromagnetic Torque ◽  
Automatic Control Systems ◽  
Optimization Of Geometry

At present time there is a need to develop a methodology for electric motors design which will ensure the optimality of their geometrical parameters according to one or a set of criterias. With the growth of computer calculating power it becomes possible to develop methods based on numerical methods for electric machines computing. The article describes method of a singlecriterion evolutionary optimization of synchronous electric machines with permanent magnets taking into account the given restrictions on the overall dimensions and characteristics of structural materials. The described approach is based on applying of a genetic algorithm for carrying out evolutionary optimization of geometric parameters of a given configuration of electric motor. Optimization criteria may be different, but in automatic control systems high requirements are imposed to electromagnetic torque electric machine produces. During genetic algorithm work it optimizes given geometric parameters of the electric motor according to the criterion of its torque value, which is being calculated using finite element method.

Data-Driven Evolutionary Optimization

2021 ◽  
Author(s):  
Yaochu Jin ◽  
Handing Wang ◽  
Chaoli Sun
Keyword(s):  
Evolutionary Optimization ◽  
Data Driven

scholarly journals Designing Strong Privacy Metrics Suites Using Evolutionary Optimization

2021 ◽  
Vol 24 (2) ◽  
pp. 1-35
Author(s):  
Isabel Wagner ◽  
Iryna Yevseyeva
Keyword(s):  
Optimization Problem ◽  
Evolutionary Optimization ◽  
Multiple Objectives ◽  
Shared Value ◽  
Vehicular Communications ◽  
Open Problems ◽  
Value Range ◽  
Genomic Privacy ◽  
Metaheuristic Optimization Algorithms ◽  
Selection Of

The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined and how. In this article, we tackle the first problem, i.e., the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e., higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.

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