Improved grey wolf optimization based on the two-stage search of hybrid CMA-ES

Zhao, Yun-Tao; Weigang, Li; Liu, Ao

doi:10.1007/s00500-019-03948-x

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…Usually, CMA-ES adopts a multivariate normal mutation distribution method to revise the covariance matrix of variables to achieve the objective function (

. There is similarity between the performance of this algorithm and the reverse matrix in the Newton method [ 28 ], however, this algorithm does not require gradient analytic computing which has difficulty finding the best solution due to lack of differentiability [ 16 ].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

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Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Alharbi

Mackenzie

Pezaros

2022

Sensors

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The emergence of the Industrial Internet of Things (IIoT) has accelerated the adoption of Industrial Wireless Sensor Networks (IWSNs) for numerous applications. Effective communication in such applications requires reduced end-to-end transmission time, balanced energy consumption and increased communication reliability. Graph routing, the main routing method in IWSNs, has a significant impact on achieving effective communication in terms of satisfying these requirements. Graph routing algorithms involve applying the first-path available approach and using path redundancy to transmit data packets from a source sensor node to the gateway. However, this approach can affect end-to-end transmission time by creating conflicts among transmissions involving a common sensor node and promoting imbalanced energy consumption due to centralised management. The characteristics and requirements of these networks encounter further complications due to the need to find the best path on the basis of the requirements of IWSNs to overcome these challenges rather than using the available first-path. Such a requirement affects the network performance and prolongs the network lifetime. To address this problem, we adopt a Covariance-Matrix Adaptation Evolution Strategy (CMA-ES) to create and select the graph paths. Firstly, this article proposes three best single-objective graph routing paths according to the IWSN requirements that this research focused on. The sensor nodes select best paths based on three objective functions of CMA-ES: the best Path based on Distance (PODis), the best Path based on residual Energy (POEng) and the best Path based on End-to-End transmission time (POE2E). Secondly, to enhance energy consumption balance and achieve a balance among IWSN requirements, we adapt the CMA-ES to select the best path with multiple-objectives, otherwise known as the Best Path of Graph Routing with a CMA-ES (BPGR-ES). A simulation using MATALB with different configurations and parameters is applied to evaluate the enhanced graph routing algorithms. Furthermore, the performance of PODis, POEng, POE2E and BPGR-ES is compared with existing state-of-the-art graph routing algorithms. The simulation results reveal that the BPGR-ES algorithm achieved 87.53% more balanced energy consumption among sensor nodes in the network compared to other algorithms, and the delivery of data packets of BPGR-ES reached 99.86%, indicating more reliable communication.

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“…Usually, CMA-ES adopts a multivariate normal mutation distribution method to revise the covariance matrix of variables to achieve the objective function (

Section: Background and Literature Reviewmentioning

confidence: 99%

“…This is a state-of-the-art optimisation technique in terms of evolutionary computation based on population methods. It has, therefore, been adopted as a standard tool for continuous optimisation in many research laboratories [ 16 ] and industrial environments worldwide.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Alharbi

Mackenzie

Pezaros

2022

Sensors

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“…The initial parameters of the ER rule evaluation model are given by experts and may not be accurate. Therefore, the CMA-ES algorithm [22][23][24] is adopted in this paper to optimize the initial parameters of the evaluation model. When solving complex nonlinear non-convex optimization problems in a continuous domain, CMA-ES has no gradient optimization, does not use gradient information, and can converge to the global optimal point in a relatively short time with fewer individuals, and is thus the most advanced algorithm in evolutionary computing.…”

Section: Cma-es Optimization Algorithmmentioning

confidence: 99%

Health Status Evaluation of Welding Robots Based on the Evidential Reasoning Rule

et al. 2023

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It is extremely important to monitor the health status of welding robots for the safe and stable operation of a body-in-white (BIW) welding production line. In the actual production process, the robot degradation rate is slow and the effective data are poor, which can reflect a degradation state in the large amount of obtained monitoring data, which causes difficulties in health status evaluation. In order to realize the accurate evaluation of the health status of welding robots, this paper proposes a health status evaluation method based on the evidential reasoning (ER) rule, which reflects the health status of welding robots by using the running state data monitored in actual engineering and through the qualitative knowledge of experts, which makes up for the lack of effective data. In the ER rule evaluation model, the covariance matrix adaptive evolutionary strategy (CMA-ES) algorithm is used to optimize the initial parameters of the evaluation model, which improved the accuracy of health status evaluations. Finally, a BIW welding robot was taken as an example for verification. The results show that the proposed model is able to accurately estimate the health status of the welding robot by using the monitored degradation data.

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“…Thus, to verify the validity of the prediction model based on the FOGJS algorithm, the original JS algorithm and other algorithms are employed to solve this optimization model. The selected comparison algorithms are the original JS algorithm [ 36 ], Aquila optimizer (AO) [ 19 ], sine cosine algorithm (SCA) [ 59 ], grey wolf optimizer (GWO) [ 60 ], rat swarm optimizer (RSO) [ 61 ], seagull optimization algorithm (SOA) [ 55 ], DE [ 28 ], and PSO algorithm [ 45 ]. Meanwhile, to reflect the efficiency of the algorithm, the number of iterations is set as 30 times to highlight the ability of the algorithm to solve problems in a short time.…”

Section: Income Forecast Model Of Rural Resident Based On Improved Je...mentioning

confidence: 99%

Fractional-Order Boosted Jellyfish Search Optimizer with Gaussian Mutation for Income Forecast of Rural Resident

Lei

Fan

Yang

et al. 2022

Computational Intelligence and Neuroscience

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The disposable income of residents can reflect the living standard of people in the area. For government departments, it is necessary to master the trend of rural resident income to formulate corresponding policies benefiting farmers. Thus, this paper proposes a grey model with an improved jellyfish search optimizer to predict the rural resident income in Shaanxi Province. Firstly, by applying fractional-order modified strategy and Gaussian mutation mechanism to the original algorithm, the proposed algorithm shows better performance in solving accuracy, stability, and convergence acceleration when compared with different classical methods on cec2017 and cec2019 test functions. Then, based on the fractional time-delayed grey model, a discrete fractional time-delayed grey model with triangular residual correction (TDFTDGM) is proposed by replacing the derivative with a first-order difference and introducing the triangular residual correction functions. Finally, the improved jellyfish search optimizer is used to explore the optimal order of the TDFTDGM model. The all-around performance of the forecast model is incomparable to additional grey models compared on four measure criteria, which means it is a practical approach for long-term prediction with small samples. Moreover, the forecast data of rural resident income in Shaanxi Province from 2021 to 2025 are given for reference.

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Improved grey wolf optimization based on the two-stage search of hybrid CMA-ES

Cited by 12 publications

References 31 publications

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation Evolution Strategy

Health Status Evaluation of Welding Robots Based on the Evidential Reasoning Rule

Fractional-Order Boosted Jellyfish Search Optimizer with Gaussian Mutation for Income Forecast of Rural Resident

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