A genetic simplified swarm algorithm for optimizing n-cities open loop travelling salesman problem
Open Loop Travelling Salesman Problem (OTSP) is one of the extension of Travelling Salesman Problem (TSP) that finding a shortest tour of a number of cities by visiting each city exactly once and do not rerurning to the starting city. In the past, TSP and OTSP has been applied in various vehicle rou...
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| 格式: | Thesis |
| 语言: | English English English |
| 出版: |
2016
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| 在线阅读: | http://eprints.uthm.edu.my/9943/2/24p%20CHIENG%20HOCK%20HUNG.pdf http://eprints.uthm.edu.my/9943/1/CHIENG%20HOCK%20HUNG%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/9943/3/CHIENG%20HOCK%20HUNG%20WATERMARK.pdf |
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| 总结: | Open Loop Travelling Salesman Problem (OTSP) is one of the extension of Travelling Salesman Problem (TSP) that finding a shortest tour of a number of cities by visiting each city exactly once and do not rerurning to the starting city. In the past, TSP and OTSP has been applied in various vehicle routing systems to optimize the route distance. However, in real-life scenario such as transportation problem does not seem similar as pictured in OTSP whereby do not all cities are required to be visited but simply restrain to several number of n cities. Therefore, a new problem called nCities Open Loop Travelling Salesman Problem (nOTSP) is proposed. In the past, Genetic Algorithm (GA) is a popular algorithm that used to solve TSPs. However, GA often suffers from premature convergence due to the difficulty in preventing the loss of genetic diversity in the population. Therefore, Genetic Simplified Swarm Algorithm (GSSA) is proposed in this srudy to overcome the drawback of GA. GSSA is an improved GA based algorithm with Simplified Swarm Optimization (SSO) algorithm's characteristic named Solution Update Mechanism (SUM). The SUM is modified by embedding three GA mutation operators. Then, GSSA is used to optimize nOTSP in terms of finding the shortest tour. Later, the performance of GSSA is compared with GA without crossover operator (GA-XX) and GA with onepoint crossover operator (GA-IX). Performance of the proposed algorithm is measured based on the shortest distance and average shortest distance found by the algorithm. Meanwhile, an investigation on influence of population size towards algorithm was also studied. The experiment results show that GSSA can discover shorter tour than GA-XX and GA-IX. Nevertheless, the study also found that most of the good solutions are discovered in the larger population sizes from 3000 to 5000. |
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