Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10254898" target="_blank" >RIV/61989100:27230/24:10254898 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001215756400001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001215756400001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.heliyon.2024.e26665" target="_blank" >10.1016/j.heliyon.2024.e26665</a>

Result language

angličtina

Original language name

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Original language description

This research introduces the Multi -Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi -objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition -Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non -dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi -objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real -world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two -bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non -dominated sorting grey wolf optimizer (NSGWO), multiobjective multi -verse optimization (MOMVO), non -dominated sorting genetic algorithm (NSGA-II), decomposition -based multiobjective evolutionary algorithm (MOEA/ D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20300 - Mechanical engineering

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Heliyon

ISSN

2405-8440

e-ISSN

2405-8440

Volume of the periodical

10

Issue of the periodical within the volume

5

Country of publishing house

GB - UNITED KINGDOM

Number of pages

33

Pages from-to

"nestrákováno"

UT code for WoS article

001215756400001

EID of the result in the Scopus database

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