A decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy
MOEA/D decomposes the multiobjective optimization problem into a number of subproblems. However, one subproblem’s requirement for exploitation and exploration varies with the evolutionary process. Furthermore, different subproblems’ requirements for exploitation and exploration are also different as the subproblems have been solved in distinct degree. This paper proposes a decomposition based multiobjective evolutionary algorithm with self-adaptive mating restriction strategy (MOEA/D-MRS). Considering the distinct solved degree of the subproblems, each subproblem has a separate mating restriction probability to control the contributions of exploitation and exploration. Besides, the mating restriction probability is updated by the survival length at each generation to adapt to the changing requirements. The experimental results validate that MOEA/D-MRS performs well on two test suites.
KeywordsMultiobjective optimization Evolutionary algorithm MOEA/D Self-adaptive mating restriction
This study was funded by China Aerospace Science and Technology Innovation Foundation (Grant number: CAST.No.JZ20160008), National Natural Science Foundation of China (Grant number: 61333003) and National Natural Science Foundation of China (Grant number: 61703382).
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
- 3.Cai X, Li Y, Fan Z, Zhang Q (2014) An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Trans Evol Comput 19:508–523Google Scholar
- 5.Chiang TC, Lai YP (2011) MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism. In: Evolutionary computation. IEEE, New Orleans, pp 1473–1480Google Scholar
- 24.Li Y, Zhou A, Zhang G (2014) An MOEA/D with multiple differential evolution mutation operators. In: Proceedings of the IEEE congress on evolutionary computation (CEC 2014), Beijing, China, 2014c. IEEE, pp 397–404Google Scholar
- 25.Lin Q et al (2017) A diversity-enhanced resource allocation strategy for decomposition-based multiobjective evolutionary algorithm. IEEE Trans Cybern 48:2388–2401Google Scholar
- 38.Sato H (2014) Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization. In: Genetic and evolutionery computation conference (GECCO 2014), Vancouver, BC, Canada ACM, pp 645–652Google Scholar
- 41.Trivedi A, Srinivasan D, Sanyal K, Ghosh A (2017) A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Trans Evol Comput 21:440–462Google Scholar
- 50.Yang S, Jiang S, Jiang Y (2016) Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes. Soft Comput 21:1–15Google Scholar
- 56.Zhang Q, Liu W, Li H (2009) The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances. In: Proceedings of the IEEE congress on evolutionary computation (CEC 09), Trondheim, Norway IEEE, pp 203–208Google Scholar
- 58.Zhang Q, Zhou A, Zhao S, Suganthan PN, Liu W, Tiwari S (2008) Multiobjective optimization test instances for the CEC 2009 special session and competition. Special session on performance assessment of multi-objective optimization algorithms, technique report. University of Essex, Colchester Technological University, SingaporeGoogle Scholar
- 65.Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the Strength Pareto evolutionary algorithm. Swiss Federal Institute of Technology (ETH) Zurich, ZurichGoogle Scholar