A Comparative Study of Four Metaheuristic Algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for Evacuation Planning

Abstract

Evacuation planning is an important activity in disaster management to reduce the effects of disasters on urban communities. It is regarded as a multi-objective optimization problem that involves conflicting spatial objectives and constraints in a decision-making process. Such problems are difficult to solve by traditional methods. However, metaheuristics methods have been shown to be proper solutions. Well-known classical metaheuristic algorithms—such as simulated annealing (SA), artificial bee colony (ABC), standard particle swarm optimization (SPSO), genetic algorithm (GA), and multi-objective versions of them—have been used in the spatial optimization domain. However, few types of research have applied these classical methods, and their performance has not always been well evaluated, specifically not on evacuation planning problems. This research applies the multi-objective versions of four classical metaheuristic algorithms (AMOSA, MOABC, NSGA-II, and MSPSO) on an urban evacuation problem in Rwanda in order to compare the performances of the four algorithms. The performances of the algorithms have been evaluated based on the effectiveness, efficiency, repeatability, and computational time of each algorithm. The results showed that in terms of effectiveness, AMOSA and MOABC achieve good quality solutions that satisfy the objective functions. NSGA-II and MSPSO showed third and fourth-best effectiveness. For efficiency, NSGA-II is the fastest algorithm in terms of execution time and convergence speed followed by AMOSA, MOABC, and MSPSO. AMOSA, MOABC, and MSPSO showed a high level of repeatability compared to NSGA-II. It seems that by modifying MOABC and increasing its effectiveness, it could be a proper algorithm for evacuation planning.

Hierarchical Behavior Model for Multi-Agent System with Evasion Capabilities and Dynamic Memory

“…Tests 13, 17, and 25 and tests 5, 9, and 21 demonstrated that if the hierarchy was changed from R-I to I-R, evacuation performance improved for both cases. Similar results were observed in both cases for hierarchy changes of S-I (tests 11,15,27) to I-S (tests 7, 19, 23) and S-R (tests 6, 10, 26) to R-S (tests 14,18,22). When the stable evacuation performance for binary combinations was sorted in a descending manner, Equation (9) for case 1 and Equation (10) for case 2 were obtained:…”

“…Different methodologies have been offered for this task by various studies. Niyomubyeyi et al [22] compared metaheuristic approaches to the evacuation problem. Musharraf et al [23] used lecture-based training to develop a decision tree for evacuating agents.…”

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

“…Regardless of the complexity in the objective functions and constraints, Genetic Algorithm converges to the optimal solution as it performs sequences of iterations. The genetic algorithm is inherently parallel for its nature of computation, and it is well known for its reliability [ 25 , 26 ].…”

Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study

“…Keshavarz et al [55] compared NSGA-II and MOPSO for the stochastic optimization of an inventory control system, showing that NSGA-II has better performance in spacing and in the number of Pareto optimal solutions, while MOPSO better spreads the fitness of the solution set and consumes fewer computational resources. Niyomubyeyi et al [56] studied optimization in evacuation planning, obtaining better convergence and spread with MOPSO, but the algorithm execution took five times longer than NSGA-II. Saldanha et al [18] obtained similar results in convergence and spread for MOPSO and NSGA-II, although MOPSO yielded better results in spacing.…”