A Hybrid Intelligent Algorithm for Stochastic Dependent-Chance Programming

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008) Pub Date : 2008-01-23 DOI:10.1109/WKDD.2008.51

Xiaoli Ning, Zeng Jianchao

引用次数: 2

Abstract

The stochastic dependent-chance programming belongs to a class of stochastic programming problems, which has wide application backgrounds, in order to search an algorithm which can more effectively solve this problem, in the paper, stochastic simulation is used to produce training samples for BP neural networks, and a hybrid intelligent algorithm for stochastic dependent-chance programming combined PSO algorithm with BP neural networks for approximation of the chance function is presented. Finally, the simulation results of two examples are given to show the correctness and effectiveness of the algorithm.

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随机相关-机会规划的混合智能算法

随机依赖机会规划是一类具有广泛应用背景的随机规划问题，为了寻找一种能更有效地解决这类问题的算法，本文采用随机模拟的方法为BP神经网络生成训练样本，提出了一种结合粒子群算法和BP神经网络的混合智能随机依赖机会规划算法，用于机会函数的逼近。最后给出了两个算例的仿真结果，验证了算法的正确性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)

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