Sensor network optimization of gearbox based on dependence matrix and improved discrete shuffled frog leaping algorithm

Abstract

This paper reports a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. A mathematical model is established to illustrate the sensor network optimization based on fault-sensor dependence matrix. The crossover and mutation operators of genetic algorithm (GA) are introduced into the update strategy of shuffled frog leaping algorithm (SFLA) and a new ID-SFLA is systematically developed. Numerical simulation results show that the ID-SFLA has an excellent global search ability and outstanding convergence performance. The ID-SFLA is applied to the sensor's optimal selection for a gearbox. In comparison with GA and discrete shuffled frog leaping algorithm (D-SFLA), the proposed ID-SFLA not only poses an effective solving method with swarm intelligent algorithm, but also provides a new quick algorithm and thought for the solution of related integer NP-hard problem.

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

“…For example in [133] the authors focused on redundant network nodes, short life cycle and network effective coverage as optimization goals, and then introduced the inverse Gaussian mutation algorithms on AFSA, made the improved algorithm to solve the model, and got the optimal coverage scheme for mobile wireless sensor networks. In [134], the authors proposed a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox, provided a new quick algorithm and thought for the solution of related integer NP-hard problem.…”

“…For example in [141] the authors proposed optimal cluster-based mechanisms by a modified multi-hop layered model for load balancing with multiple mobile sinks for these problems, and analyzed the cluster lifetimes in different situations and achieve balance to save significant energy. In [134] the authors proposed an ant-based routing protocol with QoS-effective data collection mechanism for MWSNs, the proposed routing protocol provides reliability by reducing the packet drop and end-toend delay when compared to the existing protocols. A velocity energy-efficient and link-aware cluster-tree (VELCT) scheme for data collection in mobile WSNs is proposed which would effectively mitigate the problems of coverage distance in [143].…”

“…Sung and Chung [155] proposed an improved particle swarm optimization (IPSO) approach to data integration optimization calculations, improved the efficiency of data fusion and prolonged the lifetime of network. In [134], the authors proposed a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox, provided a new quick algorithm and thought for the solution of related integer NP-hard problem.…”

“…A method is proposed to improve the quality of MWSNs data fusion by optimizing the weights and thresholds of neural networks, the BP neural network optimized by artificial fish swarm algorithm (AFSABP) neural network data fusion, the proposed algorithm has significant improvement in convergence speed and optimization accuracy [162]. In [134] the authors proposed a new improved discrete shuffled frog leaping algorithm (ID-SFLA) and its application in multi-type sensor network optimization for the condition monitoring of a gearbox. Abirami and Anandamurugan [163] proposed a shuffled frog meta-heuristic algorithm for cluster heads selection.…”

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

“…For example, a new improved SFLA was proposed in Ref. [152] and was applied to the multi-type sensor network optimization problem of gearboxes. He, et al [153], proposed an adaptive EA based on GA to optimize the sensor placement for cutting process condition monitoring.…”