Evolutionary Model Based Organization Risk Optimization: A Hybrid Approach
摘要
Risk management is very crucial today especially because there are many uncertainties that organizations have to deal with. This research paper introduces a new way to optimize risk management using a combination of two advanced techniques: Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO). Our perspective reflects various parameters of risks including potential, consequence and likelihood, exposure and cost of risk. These features of risk have been used to find out the optimum level of each feature so that the combined risk does not exceed a threshold value. In this way, maintaining a balanced objective function, the most optimal strategies that would minimize risks and costs as far as possible have been obtained. From the simulations with varieties of parameters, it has been proved that the hybrid model offers a marked improvement to the way organizations can handle risks, giving them direct, actionable solutions to help them better their risk management.