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

Content Provider	IEEE Xplore Digital Library
Author	Xin-Lan Liao Chuan-Kang Ting
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi 621, Taiwan (Xin-Lan Liao; Chuan-Kang Ting)
Abstract	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.
Starting Page	1
Ending Page	8
File Size	1108528
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6252884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Genetic algorithms Biological cells Evolutionary computation Genetics Space exploration
Content Type	Text
Resource Type	Article

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