Immune optimization algorithm for nonlinear multi-objective probabilistic constrained programming

ZHANG Renchong; ZHANG Zhuhong

doi:10.13700/j.bh.1001-5965.2019.0350

Volume 46 Issue 5

May 2020

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Journal of Beijing University of Aeronautics and Astronautics > 2020 > 46(5): 900-914.

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ZHANG Renchong, ZHANG Zhuhong. Immune optimization algorithm for nonlinear multi-objective probabilistic constrained programming[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 900-914. doi: 10.13700/j.bh.1001-5965.2019.0350(in Chinese)

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Immune optimization algorithm for nonlinear multi-objective probabilistic constrained programming

doi: 10.13700/j.bh.1001-5965.2019.0350

ZHANG Renchong¹,
ZHANG Zhuhong^{2
,
,}

1.
Computer and Information Engineering College, Guizhou University of Commerce, Guiyang 550014, China
2.
College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

Funds:

National Natural Science Foundation of China 61563009)

Youth Science and Technology Talent Development Project of Education Department in Guizhou Province QJH KY Zi[2018] No.276

Guizhou Big Data Application Engineering Research Center in Guizhou Province QJH KY Zi[2017] No.022

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Corresponding author: ZHANG Zhuhong, E-mail: zhzhang@gzu.edu.cn
Received Date: 03 Jul 2019
Accepted Date: 27 Sep 2019
Publish Date: 20 May 2020

Abstract

Abstract

This paper investigates a Multi-Objective Immune Optimization Algorithm (MOIOA) based on danger theory to solve the problem of nonlinear Multi-Objective Probabilistic Constrained Programming (MOPCP) with unknown noise information. In the design of the algorithm, adaptive sampling methods are used to estimate each chance constraint's probability and objective values, while each evolving population is divided into infected, susceptible and uninfected sub-populations in terms of one specific immune response mechanism contained by danger theory. The capability of global and local search can be enhanced, relying upon simulated binary crossover, adaptive mutation probability and polynomial mutation strategy. Numerical experiment results show that the proposed multi-objective algorithm has high efficiency and has some advantages over seven comparative methods with regard to solution quality. It has application potential to complex engineering problems.
- Multi-Objective Probabilistic Constrained Programming (MOPCP),
- immune optimization,
- danger theory,
- adaptive sampling,
- stochastic simulation

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References

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