Portfolio Optimization with Translation of Representation for Transport Problems

Malgorzata ZAJECKA; Mateusz MASTALERCZYK; Siang Yew CHONG; Xin YAO; Joanna KWIECIEN; Wojciech CHMIEL; Jacek DAJDA; Marek KISIEL-DOROHINICKI; Aleksander BYRSKI

doi:10.2478/jaiscr-2025-0004

Portfolio Optimization with Translation of Representation for Transport Problems

Malgorzata ZAJECKA, Mateusz MASTALERCZYK, Siang Yew CHONG, Xin YAO, Joanna KWIECIEN, Wojciech CHMIEL, Jacek DAJDA, Marek KISIEL-DOROHINICKI, Aleksander BYRSKI^*

^*Corresponding author for this work

School of Data Science

Research output: Journal Publications › Journal Article (refereed) › peer-review

Abstract

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 GAis possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.

Original language	English
Pages (from-to)	57-75
Number of pages	19
Journal	Journal of Artificial Intelligence and Soft Computing Research
Volume	15
Issue number	1
Early online date	8 Dec 2024
DOIs	https://doi.org/10.2478/jaiscr-2025-0004
Publication status	Published - Jan 2025

Bibliographical note

Publisher Copyright:
© 2025 Malgorzata Zajecka et al., published by Sciendo.

Keywords

metaheuristics
portfolio optimization
transport problems

Access to Document

10.2478/jaiscr-2025-0004Licence: CC BY-NC-ND

https://www.sciendo.com/article/10.2478/jaiscr-2025-0004

Cite this

@article{815b88df258343fe8ee17fe6b54cb805,

title = "Portfolio Optimization with Translation of Representation for Transport Problems",

abstract = "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 GAis possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.",

keywords = "metaheuristics, portfolio optimization, transport problems",

author = "Malgorzata ZAJECKA and Mateusz MASTALERCZYK and CHONG, {Siang Yew} and Xin YAO and Joanna KWIECIEN and Wojciech CHMIEL and Jacek DAJDA and Marek KISIEL-DOROHINICKI and Aleksander BYRSKI",

note = "Publisher Copyright: {\textcopyright} 2025 Malgorzata Zajecka et al., published by Sciendo.",

year = "2025",

month = jan,

doi = "10.2478/jaiscr-2025-0004",

language = "English",

volume = "15",

pages = "57--75",

journal = "Journal of Artificial Intelligence and Soft Computing Research",

issn = "2083-2567",

publisher = "De Gruyter Open Ltd.",

number = "1",

}

TY - JOUR

T1 - Portfolio Optimization with Translation of Representation for Transport Problems

AU - ZAJECKA, Malgorzata

AU - MASTALERCZYK, Mateusz

AU - CHONG, Siang Yew

AU - YAO, Xin

AU - KWIECIEN, Joanna

AU - CHMIEL, Wojciech

AU - DAJDA, Jacek

AU - KISIEL-DOROHINICKI, Marek

AU - BYRSKI, Aleksander

PY - 2025/1

Y1 - 2025/1

N2 - 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 GAis possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.

AB - 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 GAis possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.

KW - metaheuristics

KW - portfolio optimization

KW - transport problems

UR - http://www.scopus.com/inward/record.url?scp=85217932884&partnerID=8YFLogxK

U2 - 10.2478/jaiscr-2025-0004

DO - 10.2478/jaiscr-2025-0004

M3 - Journal Article (refereed)

SN - 2083-2567

VL - 15

SP - 57

EP - 75

JO - Journal of Artificial Intelligence and Soft Computing Research

JF - Journal of Artificial Intelligence and Soft Computing Research

IS - 1

ER -

Portfolio Optimization with Translation of Representation for Transport Problems

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this