Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development

Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical program- ming model is presented to predict the change propagation impact quantitatively. As the foundation of change propa- gation prediction, a design change analysis model（DCAM） is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity（CPI） is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by tour assessment factors： propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimiza- tion（ACO） algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The pro- posed change propagation prediction method is verified tobe efficient and effective, which could provide different results according to various the initial changes.

Fast DOA estimation algorithm for MIMO sonar based on ant colony optimization

The maximum likelihood （ML） estimator demonstrates remarkable performance in direction of arrival （DOA） estimation for the multiple input multiple output （MIMO） sonar. However, this advantage comes with prohibitive computational complexity. In order to solve this problem, an ant colony optimization （ACO） is incorporated into the MIMO ML DOA estimator. Based on the ACO, a novel MIMO ML DOA estimator named the MIMO ACO ML （ML DOA estimator based on ACO for MIMO sonar） with even lower computational complexity is proposed. By extending the pheromone remaining process to the pheromone Gaussian kernel probability distribution function in the continuous space, the pro- posed algorithm achieves the global optimum value of the MIMO ML DOA estimator. Simulations and experimental results show that the computational cost of MIMO ACO ML is only 1/6 of the MIMO ML algorithm, while maintaining similar performance with the MIMO ML method.

Ant Colony Optimization as a Powerful Tool for Descriptor Selection in QSPR Study of Infinite Dilution Activity Coefficients of Halogenated Hydrocarbons in Water

A quantitative structure-property relationship （QSPR） study was suggested for the prediction of infinite dilution activity coefficients of halogenated hydrocarbons, γ∞ , in water at 298.15 K. After optimization of 3D geometry of the halogenated hydrocarbons with semi-empirical quantum chemical calculations at the AM1 level, different descriptors （1514 descriptors） were calculated by the HyperChem and Dragon softwares. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ant colony optimization （ACO） algorithm was proposed to select the best descriptors. Then the selected descriptors were applied for model development using multiple linear regression. The average absolute relative deviation and correlation coefficient for the training set were obtained as 4.36% and 0.951, respectively, while the corresponding values for the test set were 5.96% and 0.929, respectively. The results showed that the applied procedure is suitable for the prediction of γ∞ of halogenated hydrocarbons in water.