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Гибридные схемы решения задач условной оптимизации с использованием метаэвристик

HYBRID METHODS FOR SOLVING CONSTRAINED OPTIMIZATION PROBLEMS USING METAHEURISTICS

Гибридные схемы решения задач условной оптимизации с использованием метаэвристик

Abstract

В статье рассматриваются гибридные схемы численного решения задач условной оптимизации, основанные на классических подходах, таких как метод штрафных функций, теория множителей Лагранжа, и метаэвристических алгоритмах. Приведён пример гибридной схемы, основанный на методе роя частиц и методе множителей Лагранжа с добавками. Представлены результаты численного эксперимента.

In the article hybrid methods of numerical solving constrained optimization problems based on classical approaches such as penalty functions method, Lagrange multipliers theory and metaheuristics are discussed. The example of hybrid method based on particle swarm optimization and augmented Lagrangian method is given. Numerical experiment results are provided.

№1(41) (2017)

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Keywords

metaheuristics, penalty functions, Lagrange multipliers, штрафные функции, метаэвристики, множители Лагранжа, условная оптимизация, constrained optimization

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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