Multi-objective integrated optimization based on evolutionary strategy with a dynamic weighting schedule

The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.

Approach of service recovery decision-making based on Bellman dynamic programming

Based on service-oriented architecture（SOA）,a Bellman-dynamic-programming-based approach of service recovery decision-making is proposed to make valid recovery decisions.Both the attribute and the process of services in the controllable distributed information system are analyzed as the preparatory work.Using the idea of service composition as a reference,the approach translates the recovery decision-making into a planning problem regarding artificial intelligence （AI） through two steps.The first is the self-organization based on a logical view of the network,and the second is the definition of evaluation standards.Applying Bellman dynamic programming to solve the planning problem,the approach offers timely emergency response and optimal recovery source selection,meeting multiple QoS （quality of service）requirements.Experimental results demonstrate the rationality and optimality of the approach,and the theoretical analysis of its computational complexity and the comparison with conventional methods exhibit its high efficiency.

A dynamic model for railway freight overbooking

In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process(dynamic programming) model for railway freight reservation was formulated and the overbooking limit level was proposed as a control policy. However, computing the dynamic programming treatment needs six nested loops and this will be burdensome for real-world problems. To break through the calculation limit, the properties of value function were analyzed and the overbooking protection level was proposed to reduce the calculating quantity. The simulation experiments show that the overbooking protection level for the lower-fare class is higher than that for the higher-fare class, so the overbooking strategy is nested by fare class. Besides, by analyzing the influence on the overbooking strategy of freight arrival probability and cancellation probability, the proposed approach is efficient and also has a good application prospect in reality. Also, compared with the existing reservation(FCFS), the overbooking strategy performs better in the fields of vacancy reduction and revenue improvement.

Study on load performance of electric motor system used in hybrid electric vehicle

The performance of the power assist, global optimization solved by dynamic programming （DP） method, Chery and Insight control strategies are analyzed using the mild parallel hybrid electric vehicle （PHEV） model based on Insight structure. The influence of the four control strategies to the load power of the electric motor system used on parallel hybrid electric vehicle is studied. It is found that 80 percent of the motor load power points are under 1/5 of the electric peak power. The motor load power of the power assist control strategy is distributed in the widest range during generating operation, and the motor load power of the global optimization control strategy has the smallest one.

Application of dynamic programming to structural repairing strategies

A model of dynamic programming for repairing strategies of concrete structures during a projected service period is proposed, which takes into account the degradation in strength of components and the probability of accidental load. This model takes the safety grade of a structural system as the state variable of repairing strategies, and incorporates economic factors including expected repair cost, property loss due to structure failure, goods and material loss due to structure failure, loss of production interrupt due to structure failure, and inspection cost in decision making. It is found that the optimal repairing strategies are sensitive to the probability of accidental loads as well as the failure costs. The practicality of the model is demonstrated by an example.

Definition and Algorithms for Reliable Steiner Tree Problem

This paper considers a new form of the Steiner tree problem that is more practical and reliable,which we call Reliable Steiner Tree(RST)problem.The authors give a detailed definition for this new problem and design both an exact algorithm and an approximation algorithm for it.The definition is based on the reliability of full components instead of Steiner vertices.The task is thus to find the most reliable full components to make up an optimum reliable Steiner tree.The exact algorithm designed for this problem utilizes a dynamic programming frame.The approximation algorithm designed in this paper exploits a local search strategy that looks for the best full component according to a selection function at a time.