• Multi-objective generalized no...

Result: Multi-objective generalized normal distribution optimization: a novel algorithm for multi-objective problems

Title:
Multi-objective generalized normal distribution optimization: a novel algorithm for multi-objective problems
Authors:
Nima Khodadadi, Ehsan Khodadadi, Farhad Soleimanian Gharehchopogh, El-Sayed M. EI-Kenawy, Pezhman Mardanpour, Weiguo Zhao, Seyedali Mirjalili
Source:
Cluster Computing. 27:10589-10631
Publisher Information:
Springer Science and Business Media LLC, 2024.
Publication Year:
2024
Subject Terms:
0211 other engineering and technologies, Social Sciences, 02 engineering and technology, Mathematical analysis, Decision Sciences, Artificial Intelligence, Estimation of distribution algorithm, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Swarm Intelligence Optimization Algorithms, Global Optimization, Multi-Objective Optimization, Distribution (mathematics), Optimization Applications, Mathematical optimization, Probabilistic Design Optimization, Computer science, Algorithm, Computational Theory and Mathematics, Computer Science, Physical Sciences, Uncertainty Quantification and Sensitivity Analysis, Nature-Inspired Algorithms, Statistics, Probability and Uncertainty, Multiobjective Optimization in Evolutionary Algorithms, Mathematics
Document Type:
Academic journal Article<br />Other literature type
Language:
English
ISSN:
1573-7543
1386-7857
DOI:
10.1007/s10586-024-04467-7
DOI:
10.60692/jqrzv-jzd27
DOI:
10.60692/64czt-mqh72
Rights:
CC BY
Accession Number:
edsair.doi.dedup.....a9df2b2e93885200df59d0a155ec3ac9
Database:
OpenAIRE

Further Information

This study introduces the Multi-objective Generalized Normal Distribution Optimization (MOGNDO) algorithm, an advancement of the Generalized Normal Distribution Optimization (GNDO) algorithm, now adapted for multi-objective optimization tasks. The GNDO algorithm, previously known for its effectiveness in single-objective optimization, has been enhanced with two key features for multi-objective optimization. The first is the addition of an archival mechanism to store non-dominated Pareto optimal solutions, ensuring a detailed record of the best outcomes. The second enhancement is a new leader selection mechanism, designed to strategically identify and select the best solutions from the archive to guide the optimization process. This enhancement positions MOGNDO as a cutting-edge solution in multi-objective optimization, setting a new benchmark for evaluating its performance against leading algorithms in the field. The algorithm's effectiveness is rigorously tested across 35 varied case studies, encompassing both mathematical and engineering challenges, and benchmarked against prominent algorithms like MOPSO, MOGWO, MOHHO, MSSA, MOALO, MOMVO, and MOAOS. Utilizing metrics such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the study underscores MOGNDO's ability to produce Pareto fronts of high quality, marked by exceptional precision and diversity. The results affirm MOGNDO's superior performance and versatility, not only in theoretical tests but also in addressing complex real-world engineering problems, showcasing its high convergence and coverage capabilities. The source codes of the MOGNDO algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes.