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Parallel Collaborative Filtering Recommendation Model Based on Two-Phase Similarity Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Self-adaptive Percolation Behavior Water Cycle Algorithm

Authors : Shilei Qiao, Yongquan Zhou, Rui Wang, Yuxiang Zhou

Published in: Intelligent Computing Theories and Methodologies

Publisher: Springer International Publishing

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Abstract

Water cycle algorithm is a new meta-heuristic optimization algorithm based on the observation of water cycle and how rivers and streams flow downhill towards the sea in the real world. In this paper, a new self-adaptive water cycle algorithm with percolation behavior is proposed. The percolation behavior is introduced to accelerate the convergence speed of proposed algorithm. At the same time, a self-adaptive rainfall process can generate the new stream, more and more new position can be explored, consequently, increasing the diversity of population. Eight typical benchmark functions are tested, the simulation results show that the proposed algorithm is feasible and effective than basic water cycle algorithm, and demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space.

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Metadata
Title
Self-adaptive Percolation Behavior Water Cycle Algorithm
Authors
Shilei Qiao
Yongquan Zhou
Rui Wang
Yuxiang Zhou
Copyright Year
2015
Book
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22179-3

Electronic ISBN: 978-3-319-22180-9

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22180-9_9

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