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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Hydrologic Cycle Optimization Part I: Background and Theory
verfasst von : Xiaohui Yan, Ben Niu
Erschienen in: Advances in Swarm Intelligence
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Abstract
A novel Hydrologic cycle Optimization (HCO) is proposed by simulating the natural phenomena of the hydrologic cycle on the earth. Three operators are employed in the algorithm: flow, infiltration, evaporation and precipitation. Flow step simulates the water flowing to lower areas and makes the population converge to better areas. Infiltration step executes neighborhood search. Evaporation and precipitation step could keep diversity and escape from local optima. The proposed algorithm is verified on ten benchmark functions and applied to a real-world problem named Nurse Scheduling Problem (NSP) with several comparison algorithms. Experiment results show that HCO performs better on most benchmark functions and in NSP than the comparison algorithms. In Part I, the background and theory of HCO are introduced firstly. And then, experimental studies on benchmark and real world problems are given in Part II.