Ship Pipe Routing Method Based on Genetic Algorithm and Cooperative Coevolution

2017 ◽  
Vol 33 (02) ◽  
pp. 122-134
Author(s):  
Zongran Dong ◽  
Yan Lin

Pipe routing is one of the most time-consuming and complicated jobs in shipbuilding design. This article presents the automatic ship pipe routing method. To improve the efficiency of single pipe routing, the fixed-length encoding genetic algorithm (GA) is first used by connecting adjacent intermediate points with generated pipe segments according to the specific routing patterns. The crossover and mutation operations are designed on the basis of this encoding as well. In case of the routing for multi pipes or pipe with branches, cooperative coevolutionary GA is adopted to route pipes harmoniously and to reduce the risk of combinatorial explosion caused by the number of pipes. During algorithm implementation and the building of cell decomposition model, the practical constraints in ship piping have been taken into account. In the end, the efficiency and feasibility of the proposed approach are illustrated by solving problems in designed test case and real ship applications.

2013 ◽  
Vol 760-762 ◽  
pp. 1293-1297
Author(s):  
Bin Wang ◽  
Yong Cheng Jiang ◽  
Jing Li

Software test is the important means that guarantee software quality and reliability, and in this respect,it plays the role that other method cannot replace. However software test is a complex process , it needs to consume huge manpower,material resources and time,which takes the 40%~50% of entire software development cost approximately . Paper presents the inherent in software test case designing based on genetic algorithm is using genetic algorithm to solve a set of optimization test cases, and the framework includes three parts which are test environment construction, genetic algorithm and the environment for test .


Author(s):  
Tao Ren ◽  
Zhi-Liang Zhu ◽  
Georgi M Dimirovski ◽  
Zhen-Hua Gao ◽  
Xiao-Huan Sun ◽  
...  

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.


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