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In engineering applications where system performance and cost-effectiveness are vital, reliability optimization of series systems is essential. In order to maximize system reliability and minimize system cost, this study investigates the use of Non-dominated Sorting Genetic Algorithms II (NSGA-II) and III for optimizing a five-component series system. To ensure practical cost estimation, the system cost is represented as a logarithmic function of component reliability. Within a certain range, component reliability limitations apply to the optimization problem. The performance of NSGA-II and NSGA-III is compared using convergence behavior, Pareto front analysis, and the hypervolume metric. The findings show that while NSGA-II attains more accurate trade-offs in specific reliability-cost scenarios, NSGA-III offers a better-distributed Pareto front. Decision-makers can develop highly dependable and cost-effective series systems with the help of this comparison study.

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Multi-objective Optimization of a Series System Using NSGA-II and NSGA-III: A Comparative Study on Reliability and Cost Trade-Offs

  • 会议论文

  • 发表日期：

    2026年

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  • Hemant Kumar,
  • Vijay Kumar Joshi,
  • R. N. Prajapati

摘要

In engineering applications where system performance and cost-effectiveness are vital, reliability optimization of series systems is essential. In order to maximize system reliability and minimize system cost, this study investigates the use of Non-dominated Sorting Genetic Algorithms II (NSGA-II) and III for optimizing a five-component series system. To ensure practical cost estimation, the system cost is represented as a logarithmic function of component reliability. Within a certain range, component reliability limitations apply to the optimization problem. The performance of NSGA-II and NSGA-III is compared using convergence behavior, Pareto front analysis, and the hypervolume metric. The findings show that while NSGA-II attains more accurate trade-offs in specific reliability-cost scenarios, NSGA-III offers a better-distributed Pareto front. Decision-makers can develop highly dependable and cost-effective series systems with the help of this comparison study.