Abstract
In this paper, we propose a new Bi-Population QUasi-Affine TRansformation Evolution (BP-QUATRE) algorithm for global optimization. The proposed BP-QUATRE algorithm divides the population into two subpopulations with sort strategy, and each subpopulation adopts a different mutation strategy to keep the balance between the fast convergence and population diversity. What is more, the proposed BP-QUATRE algorithm dynamically adjusts scale factor with a linear decrease strategy to make a good balance between exploration and exploitation capability. We compare the proposed algorithm with two QUATRE variants, PSO-IW, and DE algorithms on the CEC2013 test suite. The experimental results demonstrate that the proposed BP-QUATRE algorithm outperforms the competing algorithms. We also apply the proposed algorithm to dynamic deployment in wireless sensor networks. The simulation results show that the proposed BP-QUATRE algorithm has better coverage rate than the other competing algorithms.
Highlights
In the last few decades, there have been many optimization demands arising from the scientific community and from various real-world applications
Experimental results and discussion A set of experiments was conducted to evaluate the performance of the proposed algorithm BP-QUasiAffine TRansformation Evolution (QUATRE) and its application to dynamic deployment in wireless sensor networks (WSN)
In BP-QUATRE, the population is divided into two subpopulations with sort strategy, and each subpopulation employs a different mutation strategy to balance between the diversity and convergence rate
Summary
In the last few decades, there have been many optimization demands arising from the scientific community and from various real-world applications. Both mutation strategies and control parameter scale factor F have significant effects on the performance of QUATRE variants. Different mutation strategies in QUATRE algorithm have different performance over various optimization problems [13] because different mutation strategy has different search ability and convergence rate.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: EURASIP Journal on Wireless Communications and Networking
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
