Optimization technique using evolutionary algorithms
performedduringpartAofmyPh.D.study.Theresearchfieldismulti-objectiveoptimizationusingevolutionaryalgorithms,andthereseachhastakenplaceinacollaborationwithAarhusUniverity,GrundfosandtheAlexandraInstitute.Myresearchsofarhasbeenfocusedontwomainareas,i)multi-objectiveevo-lutionaryalgorithms(MOEAs)withdifferentvariation...
Multi-objective optimization using evolutionary algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study 来自 dx.doi.org 喜欢 0 阅读量: 571 作者:E Zitzler,L Thiele 摘要: Since 1985 various evolutionary approaches to multiobjective optimization have been developed, capable of searching for multiple solutions concurrently in...
Evolutionary algorithms (EAs) are useful tools in design optimization. Due to their simplicity, ease of use, and suitability for multi-objective design optimization problems, EAs have been applied to design optimization problems from various areas. In this paper we review the recent progress in desi...
First, the trigeneration system is modeled and analyzed from a thermoeconomic point of view and then multi-objective optimization using genetic algorithms is conducted to determine optimal design parameters. The design parameters considered are SOFC inlet temperature (Ti), fuel utilization factor (Uf),...
(1997). "Using evolutionary algorithms and simulation for the optimization of manufacturing systems". In: IIE Transactions. Vol. 29, 3, pp. 181-190.Pierreval, H. and Tautou, L., 1997, “Using evolutionary algorithms and simulation for the optimization of manufacturing systems”, IIE ...
GA are a class of evolutionary algorithms, hence both iterative and population-based20. On each iteration, they work with several solutions collectively called a population rather than a single solution. A GA initialises a random population as the solution and updates this solution with the help ...
Over the last two decades, evolutionary computation (EC) has shown tremendous success for solving complex real-world problems. Although the great success for EC was first recognized in the 1980s, the researchers in other domain are still confused about the acceptability of evolutionary algorithms (...
This article presents the general principles of Evolutionary Algorithms (EAs), along with a series of applications in the field of aeronautics. Classical EAs are good enough for problems based on simple mathematical models (i.e. linear models). However, as the applications evolve in complexity, ...