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A back propagation (BP) neural network-based linear constrained optimization method(BPNN-LCOM) was proposed for to solve the problems in linear constraint black box in this paper,hoping to improve the shortcoming of BP neural network-based constrained optimization method (BPNN-COM).In view of minimizing the mathematic model of network output, the basic ideas of BPNN-LCOM wereilluminated,includingmodel design and training, and BP neural network-based global optimization. Firstly, the iteration step size was calculated by optimal step size, and the adjustment step size was calculated by interpolation method, also the iteration speed was accelerated. Secondly, the search direction that iteration point locates on the boundary offeasible region was determined by gradient projection method, which ensured that the iteration process continued along a feasible search direction, and effectively solved the defect of BPNN-COM that sometimes fails to find thetrue optimal solutions. At the same time, the iteration step size along the gradient projection direction was calculated by the optimal constraint step size, which ensured the new iteration point located in the feasible region. Thirdly, the Kuhn-Tucker conditions were introduced to verify whether the iteration point is theoptimization solution that locates on the boundary of feasible region, and it made the termination criterion perfect for BPNN-LCOM.The computation results of two examples showed the effectiveness and feasibility of BPNN-LCOM. The BPNN-LOCM was used to optimize the roller-type bailing mechanism,and the optimal parameters were obtained as follows: round disc diameter was 360 mm, rotationalspeed of the steel rollerwas 250 rpm, feeding quantity was1.7 kg/s, and length-width ratio was 0.8. The corresponding minimum power consumption was 45.8 kJ/bundle. The optimization results were superior to regression analysis and BPNN-COM.The verification test was carried out and the optimization results could improve roller-type bailing mechanism. Verification results showed that the BPNN-LCOM is a feasible method for solving problems in linear constraint black box.INDEX TERMS BP neural network, Optimization method, Linear constraint, Gradient projection method.

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The brain storm optimization algorithm(BSO) is a population based metaheuristic algorithm inspried by the human conferring process that was proposed in 2010. Since its first implementation, BSO has been widely used in various fields. In this paper, we propose an agglomerative greedy brain storm optimization algorithm (AG-BSO) to solve classical traveling salesman problem(TSP). Due to the low accuracy and slow convergence speed of current heuristic algorithms when solving TSP, this paper consider four improvement strategies for basic BSO. First, a greedy algorithm is introduced to ensure the diversity of the population. Second, hierarchical clustering is used in place of the k-means clustering algorithm in standard BSO to eliminate the noise sensitivity of the original BSO algorithm when solving TSP. Exchange rules for the individuals in the population individuals were introduced to improve the efficiency of the algorithm. Finally, a heuristic crossover operator is used to update the individuals. In addition, the AG-BSO algorithm is compared with the genetic algorithm (GA), particle swarm optimization (PSO), the simulated annealing(SA) and the ant colony optimization (ACO) on standard TSP data sets for performance testing. We also compare it with a recently improved version of the BSO algorithm. The simulations show the encouraging results that AG-BSO greatly improved the solution accuracy, optimization speed and robustness.INDEX TERMS brain storm optimization algorithm, traveling salesman problem, hierarchical clustering, optimization algorithm, combinatorial optimization.

This study performed field surveys and investigated the formal and technological characteristics of traditional buildings in the Southern area of Hubei province, China, and reviewed the related historical archives and literature. This study is helpful for architects or urban administrators to understand how traditional vernacular buildings contribute to energy saving. After field surveying and mapping of these traditional buildings, this study found that the materials and the constructions of traditional buildings as well as the pattern and the layout are different from current buildings, which might lead to the high effective energy saving of traditional houses. This type of architecture can effectively improve the living environment through adjusting temperature, and improve illumination and ventilation according to the conducted testing. Also, the thermal and heat environments of traditional vernacular buildings are analyzed using BIM software. The results of the analysis demonstrate that these type of buildings can maintain great thermal and heat environment by similar building structures and materials, which also could meet the current standards in Hubei. These works demonstrate that traditional vernacular buildings could be as samples for architects when they want to make an effort on green building.