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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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/142997 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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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