Improved genetic algorithm approach for coordinating decision-making in technological disaster management

The increasing frequency of technological events has resulted in significant damage to the environment, human health, social stability, and economy, driving ongoing scientific development and interest in emergency management (EM). Traditional EM approaches are often inadequate because of incomplete and imprecise information during crises, making fast and effective decision-making challenging. Computational Intelligence techniques (CI) offer decision-supporting capabilities that can effectively address these challenges. However, there is still a need for deeper integration of emerging computational intelligence techniques to support evidence-based decision-making while also addressing gaps in metrics, standards, and protocols for emergency response and scalability. This study presents a coordinated decision-making system for multiple types of emergency case scenarios for technological disaster management based on CI techniques, including an Improved Genetic Algorithm (IGA), and Multi-objective Particle Swarm Optimization (MOPSO). The IGA enhances emergency performance by optimizing the task assignment for multiple agents involved in emergency response with coordination mechanisms, resulting in an approximately 15% improvement compared to other state-of-the-art methods. Ultimately, this study offers a promising foundation for future research to develop effective strategies for mitigating the impact of technological disasters on society and the environment.