A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser
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A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.
KeywordsMulti-objective particle swarm optimization Kriging meta-model Trapezoid index Deepwater composite riser
This work was supported by the National Natural Science Foundation of China (Grant 11572134).
- 2.Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. Computer Engineering and Networks Laboratory (TIK)-Report 103, Switerland, May (2001)Google Scholar
- 3.Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, 1942–1948 (1995)Google Scholar
- 7.Abido, M.A.: Two-level of nondominated solutions approach to multiobjective particle swarm optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2007), London, UK, 726–733. Assoc Computing Machinery, New York, July, 2007Google Scholar
- 8.Branke, J., Mostaghim, S.: About selecting the personal best in multi-objective particle swarm optimization. In: Proceedings of the 9th International Conference on Parallel Problem Solving from Nature (PPSNIX), Reykjavik, Iceland, 523–532. Springer, Berlin (2006)Google Scholar
- 11.Mostaghim, S., Teich, J.: Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization. In: Swarm Intelligence Symposium 2003, Indianapolis, USA (2003)Google Scholar
- 17.Singh, P., Couckuyt, I., Ferranti, F., et al.: A constrained multi-objective surrogate-based optimization algorithm. In: Proceedings of 2014 IEEE Congress on Evolutionary Computation (CEC), 3080–3087. IEEE (2014)Google Scholar