D2MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance
D2MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leader’s archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D2MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.
Unable to display preview. Download preview PDF.
- 3.Awwad Shiekh Hasan, B., Gan, J.Q., Zhang, Q.: Multi-objective evolutionary methods for channel selection in brain-computer interfaces: some preliminary experimental results. In: WCCI. IEEE (2010)Google Scholar
- 4.Wang, Z., Durst, G.L., Eberhart, R.C., Boyd, D.B., Ben Miled, Z.: Particle swarm optimization and neural network application for qsar. In: Parallel and Distributed Processing Symposium, International, vol. 10, p. 194 (2004)Google Scholar
- 6.Al Moubayed, N., Petrovski, A., McCall, J.: Multi-objective optimisation of cancer chemotherapy using smart pso with decomposition. In: 3rd IEEE Sym. Comp. Intel. IEEE (2011)Google Scholar
- 8.Baltar, A.M., Fontane, D.G.: A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality. In: The Twenty Sixth Annual American Geophysical Union Hydrology Days (2006)Google Scholar
- 9.Hassan, R., Cohanim, B., de Weck, O., Venter, G.: A comparison of particle swarm optimization and the genetic algorithm. In: Structural Dynamics and Materials, Texas, USA (2005)Google Scholar
- 11.Martínez, S.Z., Coello, C.A.C.: A multi-objective particle swarm optimizer based on decomposition. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011. ACM (2011)Google Scholar
- 13.Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
- 15.El-Ghazali, T.: Metaheuristics: from design to implementation. John Wiley & Sons (2009)Google Scholar