A Binary Adaptive Clone Shuffled Frog Leaping Algorithm for Three‐Dimensional Low‐Energy Target Coverage Optimization in Environmental Monitoring Wireless Sensor Networks

Abstract

In recent years, more and more researchers have paid attention to the three-dimensional target coverage of environmental monitoring wireless sensor networks (EMWSNs) under real environmental conditions. However, the target coverage method studied in the traditional two-dimensional plane is full of loopholes when applied in the real three-dimensional physical world. Most coverage algorithms usually only optimize for a single problem of target coverage or network energy consumption and cannot reduce network energy consumption while improving coverage. This paper proposes a novel binary adaptive clone shuffled leapfrog algorithm (BACSFLA) suitable for EMWSNs. BACSFLA has an excellent performance in the coverage of three-dimensional nodes, which can significantly reduce the network energy consumption of ENWSNs in the coverage process, and greatly improve the coverage of nodes. Through simulation experiments, BACSFLA was compared with simulated annealing (SA) and genetic algorithm (GA) in the same conditional parameters. The coverage rate of BACSFLA in EMWSNs is 3.9 % higher than that of GA and 5.4 % higher than that of SA. The network energy consumption of BACSFLA is 36.0 % lower than GA and 35.9 % lower than SA. Moreover, BACSFLA can significantly reduce the calculation time and get better results in a shorter time.

A modified shuffled frog leaping algorithm with inertia weight

“…This algorithm was motivated by the predatory habit of frog groups in a small pond and contains elements of local search and global information shuffling [1,2]. Due to its advantages of fast computation and excellent convergence performance, SFLA has been widely applied in optimization domains, such as parameter estimation [3], the unit commitment problem [4], wireless sensor networks (WSNs) design [5], integrated circuits design [6], scheduling problem [7], and machine learning [8]. However, with the increasing of the complexity and the dimension of the solving problem, the convergence speed and solution accuracy of SFLA decreases significantly, even the SFLA easily traps into local optima.…”

A modified shuffled frog leaping algorithm with inertia weight

“…This algorithm was motivated by the predatory habit of frog groups in a small pond and contains elements of local search and global information shuffling 10,11 . Due to its advantages of fast computation and excellent convergence performance, SFLA has been widely applied in optimization domains, such as parameter estimation 12 , the unit commitment problem 13 , wireless sensor networks (WSNs) design 14 , integrated circuits design 15 , scheduling problem 16 , and machine learning 17 . However, with the increasing complexity and the dimension of the solving problem, the convergence speed and solution accuracy of SFLA decreases significantly, even the SFLA easily traps into local optima.…”