Leader selection based Multi-Objective Flow Direction Algorithm (MOFDA): A novel approach for engineering design problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257943" target="_blank" >RIV/61989100:27240/25:10257943 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2590123024019133" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123024019133</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rineng.2024.103670" target="_blank" >10.1016/j.rineng.2024.103670</a>
Alternative languages
Result language
angličtina
Original language name
Leader selection based Multi-Objective Flow Direction Algorithm (MOFDA): A novel approach for engineering design problems
Original language description
Addressing complex real-world issues with conflicting objectives is a significant challenge in optimization. Practical algorithms must balance these objectives, mainly when decision-maker preferences are unclear. This paper introduces a multi-objective adaptation of the Flow Direction Algorithm (FDA) to address the shortcomings of traditional evolutionary and meta-heuristic optimization methods in multi-objective optimization (MOO). These conventional methods often fail to find Pareto optimal solutions and to represent all objectives fairly. Building on the FDA's success in single-objective tasks, we expanded its application to MOO, creating the MultiObjective Flow Direction Algorithm (MOFDA). MOFDA incorporates new mechanisms to accurately and uniformly find optimal solutions for MOO challenges. It features a fixed-size external archive to maintain Pareto optimal solutions, uses a grid mechanism to improve non-dominated solutions within this archive, and implements a leader selection process to guide searches in the multi-objective space. These strategies enable MOFDA to discover superior solutions and ensure extensive coverage of the Pareto front. We validated MOFDA's effectiveness by testing it against 27 diverse problems using seven performance metrics. The results show MOFDA's ability to outperform well-known algorithms, achieving significant convergence and broad coverage, thus demonstrating its advanced capability in multi-objective optimization. The MOFDA source code is available at: https://nimakhodadadi.com/algorithms-%2B-codes.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
O - Projekt operacniho programu
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Results in Engineering
ISSN
2590-1230
e-ISSN
2590-1230
Volume of the periodical
2025
Issue of the periodical within the volume
25
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
23
Pages from-to
1-24
UT code for WoS article
001391236100001
EID of the result in the Scopus database
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