Immune Algorithm for Selecting Optimum Services in Web Services Composition

For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.

Evaluation System and Actualization of Selection Supplier in ERP Enterprise

Suppliers become an essential factor of enterprise competitiveness. The analytic hierarchy process is a kind of Decision-making method with the combination of quantity and quality. This paper presents the evaluation system of supplier selection, using Analytic Hierarchy Process (AHP) method to select the optimum supplier in Enterprise Resource Planning (ERP) enterprise. In order to reduce factor of manmade and increase exactitude, this paper innovates to use a real database in ERP system for evaluation. It also gives an example that one enterprise selects supplier.

Study on the Rotor Spinning Processing of Spun Silk/Cashmere Blended Yarn

This paper studies rotor spinning blended yam produced of spun silk and cashmere. Nine samples were spun, from three different opening rollers and different navels. According to the Uster test results of yam quality properties, optimum selection is done by using analysis method of combining fuzzy decision-making and fuzzy pattern classification. Experimental plan is designed based on universal rotated experimental design, and the method of confined optimization is used to optimize the speed of opening roller, the speed of rotor and twist factor, according to the results of yam quality test by Uster.

Multi-objective particle swarm optimization by fusing multiple strategies

To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.

Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

To achieve efficient structural design,it is crucial to reduce the cost of materials while ensuring structural safety.This study proposes an optimization method for design parameters(DPs)in a prestressed steel structure driven by multi-factor coupling.To accomplish this,a numerical proxy model of prestressed steel structures is established with integration of DPs and mechanical parameters(MPs).A data association-parameter analysis-optimization selection system is established.A correlation between DPs and MPs is established using a back propagation(BP)neural network.This correlation provides samples for parameter analysis and optimization selection.MPs are used to characterize the safety of the structure.Based on the safety grade analysis,the key DPs that affect the mechanical properties of the structure are obtained.A mapping function is created to match the MPs and the key DPs.The optimal structural DPs are obtained by setting structural materials as the optimization objective and safety energy as the constraint condition.The theoretical model is applied to an 80-m-span gymnasium and verified with a scale test physical model.The MPs obtained using the proposed method are in good agreement with the experimental results.Compared with the traditional design method,the design cycle can be shortened by more than 90%.Driven by the optimal selection method,a saving of more than 20% can be achieved through reduction of structural material quantities.Moreover,the cross-sectional dimensions of radial cables have a substantial influence on vertical displacement.The initial tension and cross-sectional size of the upper radial cable exhibit the most pronounced impact on the stress distribution in that cable.The initial tension and cross-sectional size of the lower radial cable hold the greatest sway over the stress distribution in that cable.