Adaptive racing ranking-based immune optimization approach solving multi-objective expected value programming
- 195 Downloads
This work investigates a bio-inspired adaptive sampling immune optimization approach to solve a general kind of nonlinear multi-objective expected value programming without any prior noise distribution. A useful lower bound estimate is first developed to restrict the sample sizes of random variables. Second, an adaptive racing ranking scheme is designed to identify those valuable individuals in the current population, by which high-quality individuals in the process of solution search can acquire large sample sizes and high importance levels. Thereafter, an immune-inspired optimization approach is constructed to seek \(\varepsilon \)-Pareto optimal solutions, depending on a novel polymerization degree model. Comparative experiments have validated that the proposed approach with high efficiency is a competitive optimizer.
KeywordsImmune optimization Multi-objective expected value programming Sample bound estimate Adaptive racing ranking Computational complexity
This work is supported by National Natural Science Foundation NSFC (61563009).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Aickelin U, Dasgupta D, Gu F (2014) Artificial immune systems. Search Methodologies. Springer US, pp 187–211Google Scholar
- Batista LS, Campelo F, Guimarães FG et al (2011) Pareto cone \(\varepsilon \)-dominance: improving convergence and diversity in multiobjective evolutionary approaches. In: Evolutionary multi-criterion optimization, Springer, Berlin, pp 76–90Google Scholar
- Bui LT et al (2005) Fitness inheritance for noisy evolutionary multi-objective optimization. In: The 7th annual conference on genetic and evolutionary computation, ACM, pp 779–785Google Scholar
- Cantú-Paz E (2004) Adaptive sampling for noisy problems. In: Genetic and evolutionary computation conference, GECCO2004, pp 947–958Google Scholar
- Chen CH (2003) Efficient sampling for simulation-based optimization under uncertainty. In: Fourth International symposium on uncertainty modeling and analysis, ISUMA’03, pp 386–391Google Scholar
- Corne DW, Jerram NR, Knowles JD et al (2001) PESA-II: region-based selection in evolutionary multiobjective optimization. In: Genetic and evolutionary computation conference, GECCO’2001, pp 283–290Google Scholar
- Drugan MM, Nowe A (2013) Designing multi-objective multi-armed bandits approaches: a study. In: International joint conference on neural networks, IJCNN, pp 1–8Google Scholar
- Hughes EJ (2001) Constraint handling with uncertain and noisy multi-objective evolution. In: Congress on evolutionary computation 2001, CEC’2001, pp 963–970Google Scholar
- Owen J, Punt J, Stranford S (2013) Kuby immunology, 7th edn. Freeman, New YorkGoogle Scholar
- Park T, Ryu KR (2011) Accumulative sampling for noisy evolutionary multi-objective optimization. In: the 13th annual conference on Genetic and evolutionary computation, ACM, pp 793–800Google Scholar
- Phan DH, Suzuki J (2012) A non-parametric statistical dominance operator for noisy multi-objective optimization. In: Simulated evolution and learning, SEAL’12, pp 42–51Google Scholar
- Shapiro A, Dentcheva D, Ruszczyński A (2009) Lectures on stochastic programming: modeling and theory. SIAM-MPS PhiladelphiaGoogle Scholar
- Trautmann H, Mehnen J, Naujoks B (2009) Pareto-dominance in noisy environments. In IEEE congress on evolutionary computation(CEC’09), pp 3119–3126Google Scholar
- Van Veldhuizen DA (1999) Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. Ph. D. Thesis, OH: Air force Institute of Technology, Technical Report No. AFIT/DS/ENG/99-01, DaytonGoogle Scholar
- Zhang W, Xu W, Liu G, et al (2015) An effective hybrid evolutionary approach for stochastic multiobjective assembly line balancing problem. J Intell Manuf 1–8. doi: 10.1007/s10845-015-1037-5
- Zheng JH et al (2004) A multi-objective genetic approach based on quick sort. Advances in Artificial Intelligence. Springer, BerlinGoogle Scholar