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<p>Disasters are regarded as widespread social diversions that harm infrastructure, the environment, and human life. It is necessary to efficiently manage and address several emergencies in order to lessen the impact of disasters. As a result, having an emergency response system that is both efficient and effective and that distributes resources and emergency services in the best possible way is essential. This research proposes a new metaheuristic optimization technique based on artificial intelligence (AI) to model this condition as an optimization problem. The distribution of emergency resources and services in a post-disaster crisis is the main focus of the proposed approach. Utilizing the numerous protective characteristics of crested porcupines, the crested porcupine optimization algorithm (CRePOA) is a nature-inspired metaheuristic algorithm that precisely optimizes a variety of optimization problems. Due to its highly significant performance, CRePOA is nominated as a high-performance optimizer. Therefore, the proposed method&#xa0;uses CRePOA to allocate resources and services. Afterwards, the empirical analysis demonstrates that CRePOA can be applied to find nearly optimal solutions for the scenarios under consideration. The Python programming language is used to simulate the proposed method, and statistical analysis and various performance metrics are used to assess the results. As a result, the CRePOA outperformed the existing algorithm with an average computation time of 3279.97&#xa0;s for scenario 16.</p>

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Crested porcupine optimization based post-disaster emergency resource allocation

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Palaniappan Sambandam,
  • R. Anand

摘要

Disasters are regarded as widespread social diversions that harm infrastructure, the environment, and human life. It is necessary to efficiently manage and address several emergencies in order to lessen the impact of disasters. As a result, having an emergency response system that is both efficient and effective and that distributes resources and emergency services in the best possible way is essential. This research proposes a new metaheuristic optimization technique based on artificial intelligence (AI) to model this condition as an optimization problem. The distribution of emergency resources and services in a post-disaster crisis is the main focus of the proposed approach. Utilizing the numerous protective characteristics of crested porcupines, the crested porcupine optimization algorithm (CRePOA) is a nature-inspired metaheuristic algorithm that precisely optimizes a variety of optimization problems. Due to its highly significant performance, CRePOA is nominated as a high-performance optimizer. Therefore, the proposed method uses CRePOA to allocate resources and services. Afterwards, the empirical analysis demonstrates that CRePOA can be applied to find nearly optimal solutions for the scenarios under consideration. The Python programming language is used to simulate the proposed method, and statistical analysis and various performance metrics are used to assess the results. As a result, the CRePOA outperformed the existing algorithm with an average computation time of 3279.97 s for scenario 16.