The effect of chaotic mapping on naked mole-rat algorithm for energy efficient smart city wireless sensor network. (November 2022)

• Record Type: Journal Article
• Authors: Singh, Supreet; Singh, Urvinder
• Is Part Of: Computers & industrial engineering. Volume 173(2022)
• Publication Date: 2022-11
• Subjects: Naked mole-rat algorithm -- Chaotic maps -- Optimization -- Swarm intelligence -- Benchmark functions -- Wireless sensor network; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108655
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: Naked mole-rat algorithm (NMRA) is a recently introduced swarm intelligent algorithm based on the matting pattern of naked mole-rats. Though the algorithm is competitive but still shows poor exploration properties and slow convergence. In the present work, chaos theory has been introduced to enhance the exploration properties of NMRA. Apart from that, parametric adaptation has been added to analyse the effect of mating factor ( λ ) using various chaotic maps (such as chebyshev map, circle map, iterative map etc.) on NMRA. Based on these chaotic maps, ten variants of NMRA are proposed, and their performance is evaluated for CEC 2005 test suite subjected to different population and dimension sets. Further, the best variant among the proposed variants is evaluated for CEC 2014 benchmark functions, 100-digit challenge problems (CEC 2019) and three engineering design problems. In addition, to check the effectiveness of proposed best variant, it is further applied to real time problem of energy efficient and prolonged lifetime design of wireless sensor network for smart city framework. The results of best proposed variant are compared with other state-of-the-art algorithms namely differential evolution with adaptive properties (JADE), whale optimization algorithm with opposition and exponential properties (OEWOA), sine cosine crow search algorithm (SCCSA), self-adaptive differential evolution (SaDE), success-history based differential evolution along with parameter adaption (SHADE), equilibrium optimizer (EO), flower pollination algorithm based on fractional order calculus (FA-FPO), Gaussian-salp swarm algorithm (GSSA), variable neighbourhood bat algorithm (VNBA), blended biogeography-based optimization (B-BBO), improved elephant herding optimization (IMEHO), original NMRA and others. Experimental results validate the performance of the proposed NMRA with Chebyshev map (CNMRA) as the best well supported by Wilcoxon rank-sum test and Friedman rank (f-rank) test, concerning other algorithms used for comparison. Highlights: Chaotic maps with NMRA to balance exploration and exploitation. Analysis of breeding probability for five different values. Performance evaluation for variable population and dimension using CEC test suites. Real world three engineering design problems. Develop energy efficient WSN for smart city framework.