Ship Pipe Routing Design Using NSGA-II and Coevolutionary Algorithm

Pipe route design plays a prominent role in ship design. Due to the complex configuration in layout space with numerous pipelines, diverse design constraints, and obstacles, it is a complicated and time-consuming process to obtain the optimal route of ship pipes. In this article, an optimized design method for branch pipe routing is proposed to improve design efficiency and to reduce human errors. By simplifying equipment and ship hull models and dividing workspace into three-dimensional grid cells, the mathematic model of layout space is constructed. Based on the proposed concept of pipe grading method, the optimization model of pipe routing is established. Then an optimization procedure is presented to deal with pipe route planning problem by combining maze algorithm (MA), nondominated sorting genetic algorithm II (NSGA-II), and cooperative coevolutionary nondominated sorting genetic algorithm II (CCNSGA-II). To improve the performance in genetic algorithm procedure, a fixed-length encoding method is presented based on improved maze algorithm and adaptive region strategy. Fuzzy set theory is employed to extract the best compromise pipeline from Pareto optimal solutions. Simulation test of branch pipe and design optimization of a fuel piping system were carried out to illustrate the design optimization procedure in detail and to verify the feasibility and effectiveness of the proposed methodology.

Automated Test Sequence Optimization Based on the Maze Algorithm and Ant Colony Algorithm

<span>With the rapid development of China train operation and control system, validity and safety of behavioral functions of the system have attracted much attention in the railway domain. In this paper, an automated test sequence optimization method was presented from the system functional requirement specification of the high-speed railway. To overcome the local optimum of traditional ant colony algorithm, the maze algorithm is integrated with the ant colony algorithm to achieve the dynamical learning capacity and improve the adaptation capacity to the complex and changeable environment, and therefore, this algorithm can produce the optimal searching results. Several key railway operation scenarios are selected as the representative functional scenarios and Colored Petri Nets (CPN) is used to model the scenarios. After the CPN model is transformed into the extensible markup language (XML) model, the improved ant colony algorithm is employed to generate the optimal sequences. The shortest searching paths are found and the redundant test sequences are reduced based on the natural law of ants foraging. Finally, the Radio Blocking Center (RBC) test platform is designed and used to validate the optimal sequence. Testing results show that the proposed method is able to optimize the test sequences and improve the test efficiency successfully.</span>

BU-Router: Researching on Techniques of Global Router

Variety of routing approaches are employed by global routers in the VLSI circuit designs. Rip-up and reroute, as a conveniently implemented method, is widely used in most of modern global routers. Maze algorithm is always performed iteratively as the final technique to eliminate overflow. Maze algorithm and its ramifications can obtain an optimum solution. However, it will cost much CPU time if being used impertinently. In this work, we present a global router called Bottom-Up Router (BU-Router), with an optimized maze algorithm, which is based on multi-source multi-sink maze. BU-Router processes not the nets but the segments of nets in a sequence ordered by the length. In the progress, segments will be fixed on the global route graph edge, when the edge is saturated, which is as the basis, also known as bottom. Then the edge will be set as a blockage, which wont accept path goes through it any more. This means the edge will push the possible congestion in the future. Besides this, BU-Router optimized cost function in two ways: make the function adaptive and congestion center avoidable. Additionally, a specific optimized maze algorithm is proposed for routing a long distance segment so as to reduce the run-time.

Engineering Change Propagation Analysis Based on Linkage Model in Complex Product Development

To handle change propagations during complex product development, an analysis framework based on linkage model was proposed. Firstly linkage was defined to encapsulate the relations among product characteristics, and methods of linkage identification from multi-dimensions and linkage model construction were given. Then how to identify change propagation paths step by step in open scene and how to identify change propagation paths with improved mouse maze algorithm in closed scene were discussed. In the following a quantitative evaluation method of change impact risk was advanced. Finally an example of carrier robot moving structure design change was provided to validate this method.