Portfolio Optimization with Translation of Representation for Transport Problems

被引：0

作者：

Zajecka, Malgorzata ^{[1
]}

Mastalerczyk, Mateusz ^{[1
]}

Chong, Siang Yew ^{[2
]}

Yao, Xin ^{[3
]}

Kwiecien, Joanna ^{[4
]}

Chmiel, Wojciech ^{[4
]}

Dajda, Jacek ^{[1
]}

Kisiel-Dorohinicki, Marek ^{[1
]}

Byrski, Aleksander ^{[1
]}

机构：

[1] AGH Univ Krakow, Fac Comp Sci, Al Mickiewicza 30, PL-30059 Krakow, Poland

[2] Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen, Peoples R China

[3] Lingnan Univ, Sch Data Sci, Hong Kong, Peoples R China

[4] AGH Univ Krakow, Dept Automat & Robot, Al Mickiewicza 30, PL-30059 Krakow, Poland

来源：

JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH | 2025年 / 15卷 / 01期

关键词：

metaheuristics; portfolio optimization; transport problems; ALGORITHM; METAHEURISTICS;

D O I：

10.2478/jaiscr-2025-0004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper presents a hybridization of two ideas closely related to metaheuristic computing, namely Portfolio Optimization (researched by Xin Yao et al.) and Translation of Representation for different metaheuristics (researched by Byrski et al.). Thus, difficult problems (discrete optimization) are approached by a sequential run through a number of steps of different metaheuristics, providing the translation of representation (since the algorithms are completely different). Therefore, close cooperation of e.g. ACO, PSO, and GA is possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.

引用

页码：57 / 75

页数：19

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