Immune optimization approach solving multi-objective chance-constrained programming

2013 ◽  
Vol 6 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Zhuhong Zhang ◽  
Lei Wang ◽  
Fei Long
Keyword(s):  
Chance Constrained Programming ◽  
Optimization Approach ◽  
Immune Optimization ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming

Noisy Immune Optimization for Chance-Constrained Programming Problems

2011 ◽  
Vol 48-49 ◽  
pp. 740-744 ◽  
Author(s):  
Zhu Hong Zhang
Keyword(s):  
Adaptive Sampling ◽  
Chance Constrained Programming ◽  
Noisy Environments ◽  
Immune Optimization ◽  
Noise Compensation ◽  
Noisy Information ◽  
Objective Chance ◽  
And Performance ◽  
Constrained Programming ◽  
Optimization Mechanism

This work puts forward a parameter-less and practical immune optimization mechanism in noisy environments to deal with single-objective chance-constrained programming problems without prior noisy information. In this practical mechanism, an adaptive sampling scheme and a new concept of reliability-dominance are established to evaluate individuals, while three immune operators borrowed from several simplified immune metaphors in the immune system and the idea of fitness inheritance are utilized to evolve the current population, in order to weaken noisy influence to the optimized quality. Under the mechanism, three kinds of algorithms are obtained through changing its mutation rule. Experimental results show that the mechanism can achieve satisfactory performances including the quality of optimization, noise compensation and performance efficiency.

scholarly journals Multi-Objective Chance Constrained Programming of Spare Parts Based on Uncertainty Theory

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 50049-50054 ◽  
Author(s):  
Yi Yang ◽  
Kunlun Wei ◽  
Rui Kang ◽  
Sixin Wang
Keyword(s):  
Uncertainty Theory ◽  
Spare Parts ◽  
Chance Constrained Programming ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming

scholarly journals A Multi-Objective Chance-Constrained Programming Approach for Uncertainty-Based Optimal Nutrients Load Reduction at the Watershed Scale

Water ◽  
2017 ◽  
Vol 9 (5) ◽  
pp. 322
Author(s):  
Xujun Liu ◽  
Mengjiao Zhang ◽  
Han Su ◽  
Feifei Dong ◽  
Yao Ji ◽  
...  
Keyword(s):  
Programming Approach ◽  
Chance Constrained Programming ◽  
Load Reduction ◽  
Watershed Scale ◽  
Multi Objective ◽  
Objective Chance ◽  
Constrained Programming

scholarly journals Multi-objective chance-constrained blending optimization of zinc smelter under stochastic uncertainty

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Yu Chen ◽  
Yonggang Li ◽  
Bei Sun ◽  
Chunhua Yang ◽  
Hongqiu Zhu
Keyword(s):  
Chance Constrained Programming ◽  
Analytic Hierarchy ◽  
Pareto Solution ◽  
Hybrid Intelligent Algorithm ◽  
Multi Objective ◽  
Roasting Process ◽  
Objective Chance ◽  
Constrained Programming ◽  
Hierarchy Process ◽  
Production Probability

<p style='text-indent:20px;'>Considering the uncertainty of zinc concentrates and the shortage of high-quality ore inventory, a multi-objective chance-constrained programming (MOCCP) is established for blending optimization. Firstly, the distribution characteristics of zinc concentrates are obtained by statistical methods and the normal distribution is truncated according to the actual industrial situation. Secondly, by minimizing the pessimistic value and maximizing the optimistic value of object function, a MOCCP is decomposed into a MiniMin and MaxiMax chance-constrained programming, which is easy to handle. Thirdly, a hybrid intelligent algorithm is presented to obtain the Pareto front. Then, the furnace condition of roasting process is established based on analytic hierarchy process, and a satisfactory solution is selected from Pareto solution according to expert rules. Finally, taking the production data as an example, the effectiveness and feasibility of this method are verified. Compared to traditional blending optimization, recommended model both can ensure that each component meets the needs of production probability, and adjust the confident level of each component. Compared with the distribution without truncation, the optimization results of this method are more in line with the actual situation.</p>

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