Particle Swarm Optimization and Differential Evolution for Derangement Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10257261" target="_blank" >RIV/61989100:27240/24:10257261 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/abstract/document/10871873" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10871873</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICNGN63705.2024.10871873" target="_blank" >10.1109/ICNGN63705.2024.10871873</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Particle Swarm Optimization and Differential Evolution for Derangement Problems

Popis výsledku v původním jazyce

Combinatorial optimization involves solving problems that can be represented by combinatorial objects. Due to the complexity of these problems, exact solutions are difficult to obtain. Consequently, nature-inspired metaheuristics have been widely used to provide approximate solutions. While nature-inspired approaches have been extensively applied to general permutation and combination problems, there is limited exploration in solving constrained permutation problems, such as derangements. This paper investigates the use of Particle Swarm Optimization (PSO) and Differential Evolution (DE) to solve derangement problems. We introduce a novel, efficient algorithm that transforms arbitrary permutations into derangements and analyze its impact on the effectiveness of PSO and DE in solving these problems. The results contribute to the application of nature-inspired metaheuristics in constrained combinatorial optimization. Finally, we note the potential of constrained permutation problems to test nature-inspired metaheuristics. ©2024 IEEE.

Název v anglickém jazyce

Particle Swarm Optimization and Differential Evolution for Derangement Problems

Popis výsledku anglicky

Combinatorial optimization involves solving problems that can be represented by combinatorial objects. Due to the complexity of these problems, exact solutions are difficult to obtain. Consequently, nature-inspired metaheuristics have been widely used to provide approximate solutions. While nature-inspired approaches have been extensively applied to general permutation and combination problems, there is limited exploration in solving constrained permutation problems, such as derangements. This paper investigates the use of Particle Swarm Optimization (PSO) and Differential Evolution (DE) to solve derangement problems. We introduce a novel, efficient algorithm that transforms arbitrary permutations into derangements and analyze its impact on the effectiveness of PSO and DE in solving these problems. The results contribute to the application of nature-inspired metaheuristics in constrained combinatorial optimization. Finally, we note the potential of constrained permutation problems to test nature-inspired metaheuristics. ©2024 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/GF22-34873K" target="_blank" >GF22-34873K: Vícekriteriální optimalizace s omezeními pomocí analýzy potenciálních ploch</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of The 3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024 : November 23-25, 2024, Bangkok, Thailand

ISBN

979-8-3315-2923-9

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Bangkok

Datum konání akce

23. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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