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Finding all Pareto optimal paths by simulating ripple relay race in multi-objective networks
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Hu, Xiao-Bing, Gu, Sheng-Hao, Zhang, Chi, Zhang, Gong-Peng, Zhang, Ming-Kong and Leeson, Mark S. (2021) Finding all Pareto optimal paths by simulating ripple relay race in multi-objective networks. Swarm and Evolutionary Computation, 64 . 100908. doi:10.1016/j.swevo.2021.100908
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Official URL: http://dx.doi.org/10.1016/j.swevo.2021.100908
Abstract
This paper proposes a novel nature-inspired method, so-called ripple-spreading algorithm (RSA) for multi-objective path optimization problem (MOPOP). Unlike most existing methods mainly capable of finding partial or approximated Pareto front, this paper focuses on calculating the complete Pareto front. This is achieved by taking advantage of the optimality principle in natural ripple-spreading phenomenon. Basically, the proposed RSA carries out a one-off ripple relay race in the route network, and then the complete Pareto front will be identified with guaranteed optimality by backtracking those Pareto non-dominated ripples (PNDRs) which reached the destination node. Theoretical analyses and comprehensive experiments show that all complete Pareto fronts of a one-to-all MOPOP can also be found in just a single run of ripple relay race, and the reported method can be further extended to calculate all Pareto optimal paths in dynamical networks, which have rarely been touched by existing MOPOP methods. Since many real-world application problems can be converted into MOPOP, the reported method has a great potential of applications.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Mathematical optimization , Evolutionary computation, Computer algorithms, Path analysis (Statistics) -- Computer programs | ||||||||
| Journal or Publication Title: | Swarm and Evolutionary Computation | ||||||||
| Publisher: | Elsevier BV | ||||||||
| ISSN: | 2210-6502 | ||||||||
| Official Date: | July 2021 | ||||||||
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| Volume: | 64 | ||||||||
| Article Number: | 100908 | ||||||||
| DOI: | 10.1016/j.swevo.2021.100908 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| RIOXX Funder/Project Grant: |
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