Evolutionary algorithms using adaptive mutation for the selective pickup and delivery problem

The selective pickup and delivery problem (SPDP) is a novel variant of the pickup and delivery problem. This problem relaxes the constraint that all pickup nodes must be visited along the route. Specifically, the SPDP aims to find the shortest route that can supply delivery nodes with required commodities from some selected pickup nodes. Selection of pickup nodes is capable of reducing the transportation cost; on the other hand, it increases the search space and difficulty in resolving the SPDP. In this study, we propose an adaptive mutation that focuses on the selection of proper pickup nodes for the SPDP. Two evolutionary algorithms (EAs), namely genetic algorithm and memetic algorithm, for the SPDP are developed as well. Experimental results show that the proposed adaptive mutation can lead to better selection of pickup nodes for shorter routes, which validates its effectiveness on improving the two EAs for the SPDP.

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