Abstract
Wireless sensor network (WSN) can effectively help us monitor the surrounding environment and prevent the occurrence of some natural disasters earlier, but we can only get the information of the surrounding environment correctly if we know the locations of nodes. How to know the exact positions of nodes is a strict challenge in WSN. Intelligent computing algorithms have been developed in recent years. They easily solve complex optimization problems, especially for those that cannot be modeled mathematically. This paper proposes a novel algorithm, named parallel whale optimization algorithm (PWOA). It contains two information exchange strategies between groups, and it significantly enhances global search ability and population diversity of the original whale optimization algorithm (WOA). Also, the algorithm is adopted to optimize the localization of WSN. Twenty-three mathematical optimization functions are accustomed to verifying the efficiency and effectiveness of the novel approach. Compared with some existing intelligent computing algorithms, the proposed PWOA may reach better results.
Highlights
Algorithms of intelligent computing have achieved rapid development, and many animal swarm phenomenon inspires people to propose related new algorithms, such as whale optimization algorithm(WOA) [1,2,3,4,5], particle swarm optimization (PSO) [6,7,8,9], grey wolf optimization (GWO) [10, 11], cat swarm optimization (CSO) [12, 13], and artificial bee colony (ABC) [14]
The Pmeantd is the dth dimensional value of the mean position of all individuals at the tth iteration. This communication strategy randomly selects the same quantity of individuals from every group, and they are replaced by the Pmean in some dimension; if the algorithm produces a better position than a group’s best position, the algorithm would update the group’s best position
3.3 Apply the parallel whale optimization algorithm (PWOA) in distance vector-hop (DV-Hop) localization method In the second phase of localization, this paper utilizes the distance that has obtained in the first phase to recognize the position of the unknown node, and it applies PWOA in solving this problem
Summary
Algorithms of intelligent computing have achieved rapid development, and many animal swarm phenomenon inspires people to propose related new algorithms, such as whale optimization algorithm(WOA) [1,2,3,4,5], particle swarm optimization (PSO) [6,7,8,9], grey wolf optimization (GWO) [10, 11], cat swarm optimization (CSO) [12, 13], and artificial bee colony (ABC) [14].
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