NDLI: Comparative Study on Bionic Optimization Algorithms for Sewer Optimal Design

Content Provider	IEEE Xplore Digital Library
Author	Lei Wang Yuwen Zhou Weiwei Zhao
Copyright Year	2009
Abstract	Sewer network as a necessary urban infrastructure plays an important role in people’s daily life. Conventional optimization techniques have significant limitations on solving the problems of sewer optimal design. Because as a high-dimensional discrete complex optimization problem, sewer optimal design is characterized by its discrete objective function and, as an integer discrete variable, its decision variable amount keeps the same pace with engineering scales. Over the last decade, various kinds of modern bionic optimization algorithms with their special advantages have been created and applied into sewer optimal design successfully. Based on previous studies, this paper analyses and compares the solution performances of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Ant Colony Algorithms (ACA) from the three aspects respectively, they are convergence, speed and complexity of algorithm. The research result shows that compared with the other two algorithms, the ACA manifests its superiority for better convergence, satisfactory speed and relatively small algorithm complexity, which are very suitable for solving the problems of sewer optimal design.
Starting Page	24
Ending Page	29
File Size	1087920
Page Count	6
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.89
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Ant colony optimization Ant Colony Algorithms Educational institutions Linear programming Particle swarm optimization Design optimization Genetic algorithms Design engineering Computer architecture Algorithm comparison Genetic Algorithms Civil engineering Sewer optimal design Particle Swarm Optimization
Content Type	Text
Resource Type	Article

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