A comparative study of the effect of parameter scalability in multi-objective metaheuristics

Abstract—Some real-world optimization problems have hundreds or even thousands of decision variables. However, the effect that the scalability of parameters has in modern multiobjective metaheuristic algorithms has not been properly studied (the current benchmarks are normally adopted with ten to thirty decision variables). In this paper, we adopt a benchmark of parameter-wise scalable problems (the ZDT test problems) and analyze the behavior of six multi-objective metaheuristics on these test problems when using a number of decision variables that goes from 8 up to 2048. The computational effort required by each algorithm in order to reach the true Pareto front is also analyzed. Our study concludes that a particle swarm algorithm provides the best overall performance, although it has difficulties in multifrontal problems.

• Type: Conference
• Year: 2008
• Where: CEC
• Authors: Juan José Durillo, Antonio J. Nebro, Carlos A. Coello Coello, Francisco Luna, Enrique Alba
• Keywords: Artificial Intelligence, CEC 2008, Decision Variables, Real-world Optimization Problems, Test Problems