A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems
This investigation develops a novel hybrid fast converging Cross-Entropy Genetic Algorithm technique for scheduling of multi-energy system (MES). The scheduling problem is a mixed integer non-linear programming problem with non-linear and non-convex constraints, due to switching and non-linear dynam...
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sg-ntu-dr.10356-1429972020-11-01T04:43:37Z A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems Dan, Mainak Srinivasan, Seshadhri Sundaram, Suresh School of Computer Science and Engineering Interdisciplinary Graduate School (IGS) 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018) Berkeley Education Alliance for Research in Singapore Engineering::Computer science and engineering Cross Entropy Method Hybrid Real-coded Genetic Algorithm This investigation develops a novel hybrid fast converging Cross-Entropy Genetic Algorithm technique for scheduling of multi-energy system (MES). The scheduling problem is a mixed integer non-linear programming problem with non-linear and non-convex constraints, due to switching and non-linear dynamics exhibited by the MES devices. A hybridization of cross entropy and genetic algorithm termed as CE-mGA is proposed for the betterment of search space exploration as well as exploitation with fast convergence. In addition, a constraint-driven mutation strategy is also introduced in GA framework for tackling the non-linear and non-convex constraints. The investigation illustrates that the proposed algorithm is able to provide a stand-0ff between exploration and exploitation with an improvement in convergence speed than hybrid real-coded genetic algorithm upon validation at Cleantech building, Singapore. NRF (Natl Research Foundation, S’pore) Accepted version 2020-07-20T06:01:02Z 2020-07-20T06:01:02Z 2018 Conference Paper Dan, M., Srinivasan, S., & Sundaram, S. (2019). A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems. Proceedings of 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018), 1807-1814. doi:10.1109/SSCI.2018.8628639 978-1-5386-9277-6 https://hdl.handle.net/10356/142997 10.1109/SSCI.2018.8628639 2-s2.0-85062768746 1807 1814 en © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/SSCI.2018.8628639. application/pdf |
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Engineering::Computer science and engineering Cross Entropy Method Hybrid Real-coded Genetic Algorithm Dan, Mainak Srinivasan, Seshadhri Sundaram, Suresh A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
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This investigation develops a novel hybrid fast converging Cross-Entropy Genetic Algorithm technique for scheduling of multi-energy system (MES). The scheduling problem is a mixed integer non-linear programming problem with non-linear and non-convex constraints, due to switching and non-linear dynamics exhibited by the MES devices. A hybridization of cross entropy and genetic algorithm termed as CE-mGA is proposed for the betterment of search space exploration as well as exploitation with fast convergence. In addition, a constraint-driven mutation strategy is also introduced in GA framework for tackling the non-linear and non-convex constraints. The investigation illustrates that the proposed algorithm is able to provide a stand-0ff between exploration and exploitation with an improvement in convergence speed than hybrid real-coded genetic algorithm upon validation at Cleantech building, Singapore. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Dan, Mainak Srinivasan, Seshadhri Sundaram, Suresh |
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Conference or Workshop Item |
author |
Dan, Mainak Srinivasan, Seshadhri Sundaram, Suresh |
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Dan, Mainak |
title |
A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
title_short |
A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
title_full |
A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
title_fullStr |
A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
title_full_unstemmed |
A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
title_sort |
hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems |
publishDate |
2020 |
url |
https://hdl.handle.net/10356/142997 |
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1683493787671724032 |