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Algorithms for generating and distributing random 3D sphere coarse aggregates were proposed. The 3D finite element mesoscale concrete model, which consists of mortar matrix and coarse aggregates, was established based on the theory of background element and material identification. The interfacial transition zone was simplified as a kind of contact between elements. Based on this model, continuous finite element code was used to simulate the process of rigid projectiles perforating concrete targets and the effect of 3 different coarse aggregate types on residual velocities was analyzed. This work indicates that the strength and density of coarse aggregates affects the residual velocity under lower and higher impact velocity respectively.

In view of the uncertainty and intermittency of wind power, this paper presents a bi-objective short-term operation model to manage wind-thermal power systems. This model takes into account both the offer cost and emission. Wind power is regarded as a random variable and is assumed to follow the beta distribution. The bi-objective particle swarm optimization (BOPSO) approach is applied to solve the bi-objective model and Pareto front is obtained. The model and the solution method are tested on a generic system. The validity of the model and the method has been approved.

In contrast to many successful applications of ant colony optimization, the theoretical foundation is rather weak. It greatly limits the application in practical problems. One problem, called solution quality evaluation, is how to quantify the performance of the algorithm. It is hardly solved by theoretical methods. Experimental analysis method based on the analysis of search space and characteristic of algorithm itself is proposed in this paper. As algorithm runs, it would produce a large number of feasible solutions. After preprocessing, they were clustered according to distance. Then, good enough set was partitioned by the results of clustering. Last, evaluation result of ordinal performance was got by using relative knowledge of statistics. As the method only uses feasible solution produced by optimization algorithm, it is independent to specific algorithm. Therefore, the proposed method can be adopted by other intelligent optimization algorithms. The method is demonstrated through traveling salesman problem.