A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
Source abstract
Abstract
Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results.
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Research context
OpenAlex topics: Energy Efficient Wireless Sensor Networks · Metaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian 350118, China
Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian 350118, China
Department of Information Technology, Haiphong Private University, Haiphong 180000, Vietnam
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China
College of Informatics, Chaoyang University of Science and Technology, Taichung 413, Taiwan
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