BSO-CMA-ES: Brain Storm Optimization Based Covariance Matrix Adaptation Evolution Strategy for Multimodal Optimization

Qu, Liang; Zheng, Ruiqi; Shi, Yuhui

doi:10.1007/978-981-16-7502-7_19

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…To evaluate the effectiveness of the DSRegPSO algorithm, we used the CEC'13 benchmark functions, which are widely used in similar works like those in [41][42][43][44][45][46][47] as a standard test suite for LSGO problems. The CEC'13 test is composed of 15 optimization functions, including the Sphere Function, Elliptic Function, Rastrigin's Function, Ackley's Function, Schwefel's Problem 1.2 Function, Rosenbrock's Function, and their variants [44].…”

Section: Contributionmentioning

confidence: 99%

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Rivera,

Guerrero-Mendez,

Lopez-Betancur

et al. 2023

Mathematics

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Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutionary and swarm intelligence algorithms and hybrids of different artificial intelligence techniques. Previous studies have shown that swarm intelligence algorithms like PSO perform poorly in high-dimensional spaces, even with algorithms focused on reducing the search space. However, we propose a modified version of the PSO algorithm called Dynamical Sphere Regrouping PSO (DSRegPSO) to avoid stagnation in local optimal regions. DSRegPSO is based on the PSO algorithm and modifies inertial behavior with a regrouping dynamical sphere mechanism and a momentum conservation physics effect. These behaviors maintain the swarm’s diversity and regulate the exploration and exploitation of the search space while avoiding stagnation in optimal local regions. The DSRegPSO mechanisms mimic the behavior of birds, moving particles similar to birds when they look for a new food source. Additionally, the momentum conservation effect mimics how birds react to collisions with the boundaries in their search space or when they are looking for food. We evaluated DSRegPSO by testing 15 optimizing functions with up to 1000 dimensions of the CEC’13 benchmark, a standard for evaluating Large-Scale Global Optimization used in Congress on Evolutionary Computation, and several journals. Our proposal improves the behavior of all variants of PSO registered in the toolkit of comparison for CEC’13 and obtains the best result in the non-separable functions against all the algorithms.

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Section: Contributionmentioning

confidence: 99%

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Rivera,

Guerrero-Mendez,

Lopez-Betancur

et al. 2023

Mathematics

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“…This could have an adverse impact on the model diagnosis accuracy. To address this issue, this paper proposes a model optimization process that employs the projection covariance matrix adaptive evolution strategy (P-CMA-ES) to optimize the model parameters [27] [28]. The model parameters that require optimization must satisfy the following conditions.…”

Section: Step5mentioning

confidence: 99%

A Fault Diagnosis Method for Wireless Sensor Network nodes based on Belief Rule Base with Adaptive Attribute Weights

Shi

Sun

et al. 2023

Preprint

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Due to wireless sensor networks (WSNs)’ harsh operating environment and ultralong operating hours, node failures are inevitable. Ensuring the dependable collection of data necessitates the utmost importance of diagnosing faults in nodes within the wireless sensor network. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. However, a solitary data feature assigned a high weight fails to effectively discriminate between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating expert knowledge initialization parameters with the extracted data features. To optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The outcomes of the case study indicate a discernible enhancement in the accuracy of WSN node fault diagnosis when compared to the traditional BRB method.

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A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

Shi,

Li,

Sun

et al. 2024

Sci Rep

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Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.

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BSO-CMA-ES: Brain Storm Optimization Based Covariance Matrix Adaptation Evolution Strategy for Multimodal Optimization

Cited by 3 publications

References 11 publications

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

A Fault Diagnosis Method for Wireless Sensor Network nodes based on Belief Rule Base with Adaptive Attribute Weights

A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

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