Genghui Li
ABSTRACT
Multifactorial 1 Optimization (MFO) has been attracting considerable attention in the evolutionary computation community. In this paper, we propose a general multi-population evolution framework (MPEF) for MFO, wherein each population has its own random mating probability (rmp) and is used for its own task. The benefits of using MPEF are twofold: 1) Various well-developed evolutionary algorithms (EAs) can be easily embedded into MPEF for solving the task(s) of MFO problems; 2) Different populations can implement different genetic material transfers. Moreover, for instantiation, we embed a powerful differential evolution algorithm, namely SHADE, into MPEF to form a multipopulation DE algorithm (MPEF-SHADE) for solving MFO problems. The experimental results on nine MFO benchmark problems show that MPEF-SHADE is significantly better than or at least competitive with other multifactorial evolution algorithms, such as MFEA, MFDE, MFPSO and AMA.
Supplemental Material
Available for Download
Supplemental files.
- A. Gupta, Y. S. Ong, and L. Feng. 2016. Multifactorial evolution: toward evolutionary multitasking. IEEE Transaction on evolutionary computation 20, 3 (2016), 343--357.Google Scholar
Cross Ref
- B. S. Da, Y. S. Ong, L. Feng, A. K. Qin, A. Gupta, Z. X. Zhu, C. K. Ting, K. Tang, and X. Yao. 2016. Evolutionary multitasking for single-objective continuous optimization: Benchmark problems, performance metrics and baseline results. Technical Report, Nanyang Technological University, 2016.Google Scholar
- J. L Ding, C. Yang, Y.C Jin, and T.Y Chai. 2018. Generalized multi-tasking for evolutionary optimization of expensive problems. IEEE Transaction on evolution computation. DOI: ttp://Google Scholar
- R. Tanabe, and A. Fukunaga. 2013. Success-history based parameter adaptation for differential evolution. In IEEE congress on Evolutionary Computation (CEC), (2013), 71--78.Google Scholar
- L. Feng, W. Zhou, L. Zhou, S.W. Jiang, J.H. Zhong, B.S. Da, Z.X. Zhu, and Y. Wang. 2017. An empirical study of multifactorial PSO and Multifactorial DE. In IEEE congress on Evolutionary Computation (CEC), (2017), 1658--1665.Google Scholar
- Q. J Chen, X. L Ma, Y.W. Sun, and Z.X Zhu. 2017. Adaptive memetic algorithm based evolutionary multi-tasking single-objective optimization. In Asia-Pacific Conference on simulated Evolution and Learning (SEAL), (2017), 462--472.Google ScholarCross Ref
Index Terms
Multipopulation evolution framework for multifactorial optimization
Recommendations
Multifactorial optimization via explicit multipopulation evolutionary framework
AbstractMultifactorial Optimization (MFO) has attracted considerable attention in the community of evolutionary computation, which aims to deal with multiple optimization tasks simultaneously by information transfer. Unfortunately, information ...
Constrained differential evolution with multiobjective sorting mutation operators for constrained optimization
The proposed constrained differential evolution framework uses nondominated sorting mutation operator based on fitness and diversity information for constrained optimization. This study proposes a new constraint differential evolution framework.Parents ...
Ensemble of clearing differential evolution for multi-modal optimization
ICSI'12: Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part IMulti-modal Optimization refers to finding multiple global and local optima of a function in one single run, so that the user can have a better knowledge about different optimal solutions. Multiple global/local peaks generate extra difficulties for the ...
Comments