An efficient multi-objective optimization approach based on the micro genetic algorithm and its application
Source abstract
Abstract
In this paper, an efficient multi-objective optimization approach based on the micro genetic algorithm is suggested to solving the multi-objective optimization problems. An external elite archive is used to store Pareto-optimal solutions found in the evolutionary process. A non-dominated sorting is employed to classify the combinational population of the evolutionary population and the external elite population into several different non-dominated levels. Once the evolutionary population converges, an exploratory operator will be performed to explore more non-dominated solutions, and a restart strategy will be subsequently adopted. Simulation results for several difficult test functions indicate that the present method has higher efficiency and better convergence near the globally Pareto-optimal set for all test functions, and a better spread of solutions for some test functions compared to NSGAII. Eventually, this approach is applied to the structural optimization of a composite laminated plate for maximum stiffness in thickness direction and minimum mass.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Multi-Objective Optimization Algorithms · Topology Optimization in Engineering · Heat Transfer and Optimization
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References (23)
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1. Coello, C.A.C., Pulido, G.T.: Multiobjective optimization using a micro-genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2001), San Francisco, California, pp. 274–282 (2001)
2. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, England (2001)
3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGAII. IEEE Trans. Evol. Comput. 2, 182–197 (2002)10.1109/4235.996017 ↗
4. Eskandari, H., Geiger, C.D.: A fast Pareto genetic algorithm approach for solving expensive multiobjective optimization problems. J. Heuristics 14, 203–241 (2008)10.1007/s10732-007-9037-z ↗
5. Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 416–423. San Mateo, California (1993)
6. Fonseca, C.M., Fleming, P.J.: Multiobjctive optimization and multiple constraint handling with evolutionary algorithms—Part II: application example. IEEE Trans. Syst. Man Cybern. A 28, 38–47 (1998)10.1109/3468.650320 ↗
7. Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE world congress on computational computation, 1, pp. 82–87. Piscataway, NJ (1994)10.1109/ICEC.1994.350037 ↗
8. Knowles, J.: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 1, 50–66 (2006)10.1109/TEVC.2005.851274 ↗
9. Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. In: SPIE proceedings: intelligent control and adaptive systems, pp. 289–296 (1989)10.1117/12.969927 ↗
10. Kursawe, F.: A variant of evolution strategies for vector optimization. In: Parallel Problem Solving from Nature, pp. 193–197. Springer, Berlin (1990)10.1007/BFb0029752 ↗
11. Liu, G.R., Tani, J., Ohyoshi, T., Watanabe, K.: Transient waves in anisotropic laminated plates. Part 1: theory; Part 2: applications. J. Vib. Acoust. 113, 230–239 (1991)10.1115/1.2930174 ↗
12. Liu, G.R., Xi, Z.C.: Elastic waves in anisotropic laminates. CRC Press, Florida (2001)
13. Liu, G.R., Han, X.: Computational inverse techniques in nondestructive evaluation. CRC Press, Florida (2003)10.1201/9780203494486 ↗
14. Lu, H., Yen, G.G.: Rank-density-based multiobjective genetic algorithm and benchmark test function study. IEEE Trans. Evol. Comput. 4, 325–343 (2003)
15. Pulido, G.T., Coello, C.A.C.: The micro genetic algorithm 2: towards on-line adaptation in evolutionary multiobjective optimization. Evolutionary Multi-Criterion Optimization Second International Conference (EMO 2003), pp. 252–266. Faro, Portugal (2003)
16. Schafer, J.D.: Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. Thesis, Nashville, TN: Vanderbilt University (1984)
17. Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Master’s thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts (1995)
18. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. IEEE Trans. Evol. Comput. 3, 221–248 (1994)
19. Tanaka, M.: GA-based decision support system for multicriteria optimization. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics-2, pp. 1556–1561 (1995)10.1109/ICSMC.1995.537993 ↗
20. Xu, Y.G., Liu, G.R., Wu, Z.P.: A novel hybrid genetic algorithm using local optimizer based on heuristic pattern move. Appl. Artif. Intell. 7, 601–631 (2001)
21. Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: The strength Pareto approach. Technical Report 43, Institute TIK. ETH Zurich, Switzerland (1998a)
22. Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms—a comparative case study. In: Fifth international conference on parallel problem solving from nature (PPSN-V): 292–301 (1998b)10.1007/BFb0056872 ↗
23. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. IEEE Trans. Evol. Comput. 8, 173–195 (2000)