This paper aims to compare and evaluate several approaches from the state-of- the-art literature for developing a Multi-objective Particle Swarm Optimization (MOPSO) Algorithm that aims to classify victims in emergency response for evacuation and generate decision support information for agents during emergency response. The paper compares the results of the proposed approach with those obtained from other state-of-the-art approaches with similar objectives to evaluate their performance and improvements. One of the critical metrics for evaluating the MOPSO algorithm is the response time of agents, which is critical in disaster situations. Other metrics may include the accuracy of victim classification, efficiency of the algorithm, and ease of implementation. Moreover, ways in which the proposed MOPSO algorithm can be adapted to other disaster events of similar nature are explored. Overall, this paper aims to provide a comprehensive evaluation of the proposed MOPSO algorithm and highlight its potential usefulness in emergency response situations.