Ensemble of neighborhood search operators for decomposition-based multi-objective evolutionary optimization

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127227 - 127227

Published: March 1, 2025

Language: Английский

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Benchmarking Real-World Many-Objective Problems: A Problem Suite With Baseline Results

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 49275 - 49290

Published: Jan. 1, 2024

In recent decades, multi-objective evolutionary algorithms (MOEAs) have been evaluated on artificial test problems with unrealistic characteristics, leading to uncertain conclusions about their efficacy in real-world applications. To address this issue, a few benchmark suites comprising proposed for MOEAs, encompassing numerous and select many-objective problems. Given the distinct challenges posed by optimization (MaOPs) inherent difficulty, it is crucial develop suite that includes many conflicting objectives. Hence, paper, we propose comprehensive benchmarking complex This consists of 11 collected from different disciplines engineering. Furthermore, comprehensively analyzed our newly suite, employing eight state-of-the-art rooted various fundamental principles specifically designed MaOPs. The experimental findings highlight strong performance indicator-based, weight-vector-based decomposition, Pareto-dominance-based, hybrid MOEAs suite. contrast, reference-vector-based decomposition approaches, Pareto front shape estimation-based methods, multi-evolution approaches exhibit relatively weaker performance.

Language: Английский

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A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimization

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121244 - 121244

Published: Aug. 31, 2023

Language: Английский

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Leadership succession inspired adaptive operator selection mechanism for multi-objective optimization

Mathematics and Computers in Simulation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Representation Auto-fused NMF based Hierarchical Clustering

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127560 - 127560

Published: April 1, 2025

Language: Английский

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An efficient multi-objective state transition algorithm based on improved crowding distance

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 128120 - 128120

Published: May 1, 2025

Language: Английский

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Clustering-Aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 120612 - 120623

Published: Jan. 1, 2024

Multiobjective evolutionary algorithms are highly effective in solving multiobjective optimization problems (MOPs). The selection strategy, involving mating and environmental selection, is crucial shaping these algorithms. However, when applied to many-objective (MaOPs) with more than three objectives, existing methods face challenges due reduced pressure issues maintaining diversity, making them less efficient. To address challenges, we present a novel approach this paper: the Clustering-aided Grid-Based One-to-One Selection-driven Evolutionary Algorithm (ClGrMOEA), designed handle both MOPs MaOPs effectively. In ClGrMOEA, introduce hybrid that combines clustering-based utilizing K-means clustering Euclidean distance-based convergence indicators, grid-based one-to-one merging Pareto dominance selection. Extensive experiments conducted on 19 benchmark 16 real-world validate superior performance of ClGrMOEA compared seven state-of-the-art experimental results demonstrate significantly outperforms

Language: Английский

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Machine learning-assisted discovery of phase transformed Al-Ni co-doping high entropy alloys for superior corrosion resistance

Journal of Alloys and Compounds, Journal Year: 2024, Volume and Issue: 1006, P. 176354 - 176354

Published: Sept. 4, 2024

Language: Английский

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Expediency Analysis of Clustering Algorithms for Electric Two-wheeler Driving Cycle Development under Indian Smart City Driving Conditions

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 180279 - 180300

Published: Jan. 1, 2024

Language: Английский

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A Novel Autoencoder-Integrated Clustering Methodology for Inventory Classification: A Real Case Study for White Goods Industry

Sustainability, Journal Year: 2024, Volume and Issue: 16(21), P. 9244 - 9244

Published: Oct. 24, 2024

This article presents an inventory classification method that provides more accurate results in the white goods factory, which will contribute to sustainability, sustainability economics, and supply chain management targets. A novel application is presented with real-world data. Two different datasets are used, these compared each other. These larger dataset Stock Keeping Unit (SKU)-based (6.032 SKUs), smaller one product-group-based (270 product groups). In first phase, Artificial Intelligence (AI) clustering methods have not been used field of classification, our knowledge, applied datasets; obtained using K-Means, Gaussian mixture, agglomerative clustering, spectral methods. second stage, autoencoder separately hybridized AI develop a approach classification. Fuzzy C-Means (FCM) third step classify inventories. At end study, nine methodologies (“K-Means, clustering” without C-Means) two datasets. It shown proposed new hybrid gives much better than classical

Language: Английский

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