Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Tang, Rui; Fong, Simon; Dey, Nilanjan; Wong, Raymond K.; Mohammed, Sabah

doi:10.3390/e19100533

Cited by 16 publications

(8 citation statements)

References 26 publications

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“…X i is generated with the help of f (., v). It is noteworthy that the CE approach find the improved sampling density f (., v * ) thus the optimum solutions could be sampled [18].…”

Section: S(xmentioning

confidence: 99%

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

Dutta¹,

Srinivasan²,

Rao³

et al. 2022

Computers, Materials &Amp; Continua

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In current days, the domain of Internet of Things (IoT) and Wireless Sensor Networks (WSN) are combined for enhancing the sensor related data transmission in the forthcoming networking applications. Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks. In this view, this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing (EMO-QoSCMR) Protocol for IoT assisted WSN. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. The proposed model involves two stage processes namely clustering and routing. Firstly, the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering (CEROAC) technique to select an optimal set of cluster heads (CHs) and construct clusters. Besides, oppositional chaos game optimization based routing (OCGOR) technique is employed for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy, distance to sink node, etc. The proposed EMO-QoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH, PSO-ECHS, E-OEERP, and iCSHS methods have resulted in a lesser NAN of 2, 10, 42, and 51 rounds. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

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“…X i is generated with the help of f (., v). It is noteworthy that the CE approach find the improved sampling density f (., v * ) thus the optimum solutions could be sampled [18].…”

Section: S(xmentioning

confidence: 99%

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

Dutta¹,

Srinivasan²,

Rao³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…CE not only solves rare event probability estimation problems. It can also be used to solve complex optimization problems such as combination optimization [ 46 , 47 , 48 ], function optimization [ 46 , 48 , 49 ], engineering design [ 50 ], vehicle routing problems [ 51 ], and problems from other fields [ 52 , 53 , 54 ].…”

Section: Preliminariesmentioning

confidence: 99%

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Liu

et al. 2019

Entropy

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Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.

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“…This optimization tuner is characterized by fast convergence, the efficiency of computation and it has the capability to find local and global solutions [ 31 , 32 ]. Other modern and generalized optimization techniques can be employed either to improve the optimization process or to make a comparison in performance among each other [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

Humaidi

Ibraheem

Azar

et al. 2020

Entropy

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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.

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Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Cited by 16 publications

References 26 publications

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles

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