Evolutionary algorithms for solving multi-objective shortest path problem -Case study of vehicle navigation problems

Finding Multi-objective shortest paths (MOSP) is an important problem in computer and transportation networks. MOSP is an NP-hard problem when it contains more than two objectives. MOSP problem can be e ciently solved using the evolutionary algorithms (EAs). The existing EAs are of two types: Population-based and single-solution-based. Population-based EAs are memory- intensive and single-solution-based EAs cannot yield good quality solutions within a small amount of time. We proposed two new EAs to solve the MOSP problem and overcome the shortcomings of the existing EAs. The proposed EAs require lesser memory and at the same time can also yield good quality solutions. The rst algorithm is based on Stochastic Evolution (StocE) and works on a single solution. It considers di erent sub-paths in the solution as its character- istics and eliminates bad sub-paths from generation to generation. The second proposed algorithm is an o -spring non-storing GA which is memory-e cient than the existing GAs and its variants. Unlike existing GA-based algorithms it does not store children chromosomes in the memory. In the proposed GA- based algorithm, the children chromosomes conditionally replace their parent chromosomes and thus do not need to be stored at new memory locations. The quality of the pareto-optimal sets of the proposed algorithms is determined by using the Hypervolume metric. This works considers two applications in which the MOSP problem occurs. The rst problem is the selection of optimal paths in the conventional vehicles and the second problem is the selection of optimal paths in the electric vehicles. The proposed algorithm outperforms the exist- ing single-solution-based EAs in solution quality and requires lesser memory than the population-based algorithms. The proposed algorithms can also be generalized to solve any multi-objective optimization problems. The proposed algorithm can solve complicated test problems of multi-objective optimization with a quality which is competitive to the existing popular EAs. The e ect of memory size on the solution quality is also studied. It is found that excessive increase in the memory size does not improve the solution quality. The exper- imental results show that the proposed StocE and GA based algorithms are highly suitable to solve the MOSP problem in embedded systems学位記番号：工博甲46

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