A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
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 optimi...
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| Published in | Applied sciences Vol. 9; no. 10; p. 1973 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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MDPI AG
01.05.2019
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| ISSN | 2076-3417 2076-3417 |
| DOI | 10.3390/app9101973 |
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| 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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| AbstractList | 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. A discrete version of BA was parallelized (called PBA) for optimizing makespan in job shop scheduling problems [12]. Another discrete BA was used to address the optimal permutation of the flow shop scheduling problem [13] and discrete BA was adopted to solve the symmetric and asymmetric Traveling salesman problem (TSP) [14]. [...]rapid growth in the field of integrated circuits (IC) and information technology (IT) has led to the development of inexpensive and tiny-size sensor nodes in a wireless sensor network (WSN). A WSN detects and measures (or monitors) a number of physical or environmental conditions such as multimedia [17,18], infrared energy emitted from objects and converted to temperature [19], pollutant levels [20], ultrasound in medical imaging [21], vibrations [22], security and surveillance [23], agriculture [24], and many other such conditions. A smaller cluster near the BS indicates a smaller number of cluster members and less intra-cluster traffic. [...]the smaller clusters can concentrate more on inter-cluster traffic, and the cluster head does not drain out of energy as quickly. |
| Author | Dao, Thi-Kien Nguyen, Trong-The Pan, Jeng-Shyang |
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| SubjectTerms | Algorithms compact bat algorithm Energy consumption Fault diagnosis Inventory control Optimization Sensors Surveillance swarm intelligence Traveling salesman problem unequal clustering Wireless networks wireless sensor networks |
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| Title | A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks |
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