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Related Experiment Video

Updated: Apr 18, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Optimal Bi-Objective Redundancy Allocation for Systems Reliability and Risk Management.

Kannan Govindan, Ahmad Jafarian, Mostafa E Azbari

    IEEE Transactions on Cybernetics
    |January 27, 2015
    PubMed
    Summary

    A new hybrid algorithm combining NSGA-II and APBSA enhances systems reliability optimization. This approach improves risk management in the big data era, outperforming existing methods.

    Related Experiment Videos

    Last Updated: Apr 18, 2026

    An R-Based Landscape Validation of a Competing Risk Model
    05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

    2.8K

    Area of Science:

    • Engineering
    • Computer Science
    • Operations Research

    Background:

    • Systems reliability is crucial for effective risk management, especially in the big data era.
    • Existing optimization algorithms may have limitations in addressing complex reliability problems.

    Purpose of the Study:

    • To develop a novel multiobjective optimization approach for systems reliability.
    • To enhance systems risk management through improved reliability optimization.

    Main Methods:

    • Hybridization of NSGA-II (Nondominated Sorting Genetic Algorithm II) and APBSA (Adaptive Population-Based Simulated Annealing).
    • Utilized a coevolutionary strategy for algorithm development.
    • Employed the response surface method for parameter estimation.
    • Compared the proposed algorithm against MOGA, NRGA, and NSGA-II using four performance measures.

    Main Results:

    • The proposed hybrid algorithm demonstrated effectiveness in systems reliability optimization.
    • Computational studies confirmed the superiority of the developed approach.
    • The algorithm showed strong performance across metrics like mean ideal distance and diversification.

    Conclusions:

    • The novel hybrid NSGA-II and APBSA approach is an effective tool for systems reliability and risk management.
    • The method offers a significant advancement for optimizing complex systems.
    • The findings are relevant for big data environments requiring robust risk management.