Title
An intelligent metaphor-free spatial information sampling algorithm for balancing exploitation and exploration
Abstract
In this paper, we propose an intelligent scheme and design a spatial information sampling algorithm (SIS) to achieve a balance between exploitation and exploration more efficiently. In SIS, by creatively using a chaotic map, we divide the population of the algorithm into external and internal parts and activate the corresponding exploitation or exploration operations according to the location where the optimal individuals appear in the population. The intelligent scheme makes full use of the spatial information in the solution space where the algorithm’s population is located. It also gives the algorithm the authority to start exploitation or exploration operations autonomously based on the information in the space, which increases the flexibility of the algorithm in complex optimization problems. This novel scheme not only further promotes the intelligence of the algorithm in a simple architecture but also creates a new way of balancing exploitation and exploration in the optimization process. We test the SIS on IEEE CEC2017, IEEE CEC2011, an artificial neural model training problem, and a position optimization problem of wave energy converters. When compared to other state-of-the-art meta-heuristics, the results of the Wilcoxon signed-rank test, Wilcoxon rank-sum test, and Friedman test revealed that SIS possesses superior solutions in terms of global optimality, avoidance of local minima, and solution quality. The source code of SIS is released at https://toyamaailab.github.io/.
Year
DOI
Venue
2022
10.1016/j.knosys.2022.109081
Knowledge-Based Systems
Keywords
DocType
Volume
Meta-heuristic algorithms,Chaotic map,Intelligence,Exploration and exploitation
Journal
250
ISSN
Citations 
PageRank 
0950-7051
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Haichuan Yang100.34
Yang Yu29455.24
Jiujun Cheng316610.39
Zhenyu Lei400.68
Zonghui Cai551.74
Zihang Zhang600.34
Shangce Gao700.68