Abstract
This study proposes a new classifying approach for identifying collected failure data of cluster head (CH) in wireless sensor networks (WSN) based on hybridizing improved multi-verse optimizer (MVO) and feedforward neural network (FNN). An improvement of the MVO is proposed based on enhancing diversity agents for avoiding it's disadvanced of the local optimal. The data failure is detected for aggregating data in CH to forward to the base station (BS) based on classification by applying hybrid improved MVO and FNN. Twelve selected benchmark functions and the problem of identifying failure data in WSN are used in conducting comprehensive experiments to evaluate the performance of the proposed method. The experimental results are investigated and compared with the other approaches in the literature. The compared result exhibits the proposed technique that provides the alternative tool with the anticipation of influence on data sets and an effective way of forwarding the correct data from CH to BS in WSN applications.
Highlights
The meta-heuristic algorithms have been one of the most considered active areas of research in the field of artificial intelligence (AI) for recent decades [1], [2]
HYBRID neural networks (NN)-improved MVO (IMVO) FOR DATA FAILURE IDENTIFICATION we present a proposed scheme of minimum error in training forward neural network (FNN) for data failure identification in cluster head (CH) of clustering wireless sensor networks (WSN) based on the IMVO
In this paper, a new training method of feed-forward neural network (FNN) for identifying failure data in cluster heads (CH) of WSN was proposed based on improving multi-verse optimizer (MVO)
Summary
The meta-heuristic algorithms have been one of the most considered active areas of research in the field of artificial intelligence (AI) for recent decades [1], [2]. MVO is a new meta-heuristic optimizer inspired by the multi-verse theory to find the best planet in the universe as solutions based on phenomena physics of white holes, black holes, and wormholes for global optimization [14]. The MVO simulates the trend forwarding to the best planet in the universe of the multi-verse theory that generates implemented solutions based on phenomena physics of white holes, black holes, and wormholes for global optimization [14]. This algorithm can effectively solve many optimization problems in real life [37]. The results are presented and discussed to further confirmation of the proposed scheme performance
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