A Novel Jaya-BAT Algorithm Based Power Consumption Minimization in Cognitive Radio Network

Kaur, Avneet; Sharma, Surbhi; Mishra, Amit

doi:10.1007/s11277-019-06509-5

Cited by 6 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xiong et al used the Differential Evolution algorithm in combination with JAYA to determine the parameters of the solid oxide fuel cell model [49]. Kaur, Sharma, and Mishra proposed the JAYA-Bat algorithm as a solution for reducing cognitive radio network power consumption [50]. Kumar and Yadav [51] combined the Teaching Learning Based Optimization algorithm with JAYA [51] and Azizi et al brought together the ant lion optimizer algorithm with JAYA [52].…”

Section: Related Workmentioning

confidence: 99%

Senior Learning JAYA With Powell’s Method and Incremental Population Strategy

Yavuz

2022

IEEE Access

View full text Add to dashboard Cite

JAYA algorithm is one a recently developed meta-heuristic algorithm that does not require algorithm-specific parameters. It is an algorithm based on the fact that the solutions always go towards the best when searching. This paper proposes a JAYA variant (JAYA-SIP) with three improvements to the Original JAYA algorithm. It has incorporated the senior learning strategy, the incremental population strategy, and Powell's local search method into JAYA. The improvements were tested with IEEE Congress on Evolutionary Computation (CEC) benchmark set for 30 and 50 dimensions, and the benchmark functions set from a special issue of the Soft Computing journal (SOCO) for 500 and 1000 dimensions. In addition to benchmark sets, the performance of JAYA-SIP was evaluated with nine CEC 2011 real-world test functions. The results of the proposed algorithm are compared with JAYA variants and some meta-heuristic algorithms. According to the results of the experiment and the analysis, the proposed improvements increased the performance of the JAYA algorithm. JAYA-SIP achieved better results than the other algorithms it was compared with.

show abstract