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An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering

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Bibliographic Details
Title: An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering
Authors: Gadallah Mohamed Abd Elaziz, Yasmine Abouelseoud, Sara H. Kamel
Source: IEEE Access, Vol 13, Pp 128337-128359 (2025)
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year: 2025
Subject Terms: incremental learning, fuzzy K-means clustering, Evolutionary algorithm, inverse modeling, Electrical engineering. Electronics. Nuclear engineering, support vector regression, fastfood approximation, TK1-9971
Description: The use of inverse modeling-based crossover operators in multi-objective evolutionary algorithms (MOEAs) has recently received much attention. Sampling in the objective space is advantageous over sampling in the decision space as it allows selecting promising areas worthy to explore. This paper aims to develop an inverse modeling MOEA based on decomposition that employs an incremental learning-based support vector regression (SVR) model, as an alternative to the Gaussian process model, in order to improve the quality of obtained solutions and speed up convergence of the algorithm. Several inverse SVR models are constructed and the samples in the objective space are partitioned among them based on fuzzy clustering instead of hard clustering to enrich the training process. Extensive simulations on various benchmark problems show that the proposed algorithm drastically reduces the number of function evaluations required to reach an optimal solution compared to existing methods. The algorithm is also tested on the pathfinding problem, the community detection problem, the sparse portfolio problem, and other real-world problems, all of which confirmed the scalability and superiority of the proposed algorithm.
Document Type: Article
ISSN: 2169-3536
DOI: 10.1109/access.2025.3590300
Access URL: https://doaj.org/article/48dfd59de4734963b8471ed2d3c3f269
Rights: CC BY
Accession Number: edsair.doi.dedup.....f9b04cbe5b2a46e970044fc058399f8c
Database: OpenAIRE
Description
Abstract:The use of inverse modeling-based crossover operators in multi-objective evolutionary algorithms (MOEAs) has recently received much attention. Sampling in the objective space is advantageous over sampling in the decision space as it allows selecting promising areas worthy to explore. This paper aims to develop an inverse modeling MOEA based on decomposition that employs an incremental learning-based support vector regression (SVR) model, as an alternative to the Gaussian process model, in order to improve the quality of obtained solutions and speed up convergence of the algorithm. Several inverse SVR models are constructed and the samples in the objective space are partitioned among them based on fuzzy clustering instead of hard clustering to enrich the training process. Extensive simulations on various benchmark problems show that the proposed algorithm drastically reduces the number of function evaluations required to reach an optimal solution compared to existing methods. The algorithm is also tested on the pathfinding problem, the community detection problem, the sparse portfolio problem, and other real-world problems, all of which confirmed the scalability and superiority of the proposed algorithm.
ISSN:21693536
DOI:10.1109/access.2025.3590300