Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization

Problem of finding optimized placement of bored concrete piles to reinforce soil slope has been considered. Processes in soil were modelled by two-dimensional strain-stress model which takes filtration processes into account. Genetic and particle swarm optimization algorithms were used to find optim...

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Datum:2017
1. Verfasser: Bohaienko, Vsevolod Oleksandrovich
Format: Artikel
Sprache:Englisch
Veröffentlicht: Кам'янець-Подільський національний університет імені Івана Огієнка 2017
Online Zugang:http://mcm-math.kpnu.edu.ua/article/view/121414
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Назва журналу:Mathematical and computer modelling. Series: Physical and mathematical sciences

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Mathematical and computer modelling. Series: Physical and mathematical sciences
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author Bohaienko, Vsevolod Oleksandrovich
author_facet Bohaienko, Vsevolod Oleksandrovich
author_sort Bohaienko, Vsevolod Oleksandrovich
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datestamp_date 2019-03-13T10:34:52Z
description Problem of finding optimized placement of bored concrete piles to reinforce soil slope has been considered. Processes in soil were modelled by two-dimensional strain-stress model which takes filtration processes into account. Genetic and particle swarm optimization algorithms were used to find optimized values of soil reinforcement parameters. Multiagent parallel algorithm is proposed to scale search process onto large parallel computational environments
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institution Mathematical and computer modelling. Series: Physical and mathematical sciences
language English
last_indexed 2025-07-17T10:42:36Z
publishDate 2017
publisher Кам'янець-Подільський національний університет імені Івана Огієнка
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spelling mcm-mathkpnueduua-article-1214142019-03-13T10:34:52Z Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization Bohaienko, Vsevolod Oleksandrovich Problem of finding optimized placement of bored concrete piles to reinforce soil slope has been considered. Processes in soil were modelled by two-dimensional strain-stress model which takes filtration processes into account. Genetic and particle swarm optimization algorithms were used to find optimized values of soil reinforcement parameters. Multiagent parallel algorithm is proposed to scale search process onto large parallel computational environments Кам'янець-Подільський національний університет імені Івана Огієнка 2017-02-06 Article Article Рецензована Стаття application/pdf http://mcm-math.kpnu.edu.ua/article/view/121414 10.32626/2308-5878.2017-16.30-40 Mathematical and computer modelling. Series: Physical and mathematical sciences; 2017: Mathematical and computer modelling. Series: Physical and mathematical sciences. Issue 16; 30-40 Математичне та комп'ютерне моделювання. Серія: Фізико-математичні науки; 2017: Математичне та комп'ютерне моделювання. Серія: Фізико-математичні науки. Випуск 16; 30-40 2308-5878 10.32626/2308-5878.2017-16 en http://mcm-math.kpnu.edu.ua/article/view/121414/116515 Авторське право (c) 2021 Vsevolod Oleksandrovich Bohaienko
spellingShingle Bohaienko, Vsevolod Oleksandrovich
Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization
title Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization
title_full Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization
title_fullStr Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization
title_full_unstemmed Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization
title_short Multiagent Heuristic Algorithms for Solving Problems of Slope Reinforcement Parameters Optimization
title_sort multiagent heuristic algorithms for solving problems of slope reinforcement parameters optimization
url http://mcm-math.kpnu.edu.ua/article/view/121414
work_keys_str_mv AT bohaienkovsevolodoleksandrovich multiagentheuristicalgorithmsforsolvingproblemsofslopereinforcementparametersoptimization