Danger theory inspired micro-population immune optimization for probabilistic constrained programming

Zhang, Zhuhong; Renchong, Zhang

doi:10.1007/s12530-019-09277-6

Cited by 1 publication

(1 citation statement)

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“…Monte Carlo stochastic simulation has been widely used in stochastic programming because of its simplicity, convenience, and suitability for noise of any distribution type. In terms of handling noise in the objective function by Monte Carlo stochastic simulation, the sampling types are broadly divided into three categories: static sampling 2,[4][5][6][7][8][31][32][33][34][35][36][37][38] , dynamic sampling 39 , and adaptive sampling 17,20,21,40 . Static sampling is a simple and easy-to-use sampling method and requires that all individuals are assigned the same and sufficiently large sample size to obtain an estimate that approximates the true value, which inevitably leads to higher computational complexity.…”

Section: Noise Handling Approachesmentioning

confidence: 99%

Adaptive Sampling Immune Algorithm for Multi-Objective Probabilistic Optimization

Zhang,

Pan,

Zhang

2024

Preprint

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In this paper, for a general type of multi-objective probabilistic optimization problem without any prior noisy information, which has an extensive engineering application background and needs to be solved urgently, we propose a small-population immune algorithm with adaptive sampling to solve. In the design. First, we design a small-population immune algorithm framework inspired by the response mechanism of adaptive immunity. Second, we design an adaptive sampling scheme that adaptively allocates an appropriate number of samples for each sub-objective function of all individuals in the population to estimate the objective function value. Third, based on the objective function estimates, the dominance levels of all individuals in the population and the crowding distances of individuals in each dominance level are determined. Fourth, the clone size, mutation rate, crossover distribution index, and mutation distribution index of an individual are designed to be adaptively determined based on the number of iterations, dominance level, and crowding distance. Cloning, crossover, and mutation operators are implemented for each individual, using simulated binary crossover and polynomial mutation to enhance co-evolution and facilitate information sharing and exchange among all individuals. Fifth, based on the dominance level and crowding distance, the population update strategies are designed to adaptively update the memory set with high-quality individuals and generate a new generation population with good diversity. Finally, based on three theoretical problems and two engineering problems, as well as six representative comparative algorithms, the experimental results show that the proposed algorithm is an optimizer with good competitiveness and application potential, and has few parameters, less sample consumption, strong noise suppression ability, and high search efficiency.

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