Multiobjective Krill Herd Algorithm for Electromagnetic Optimization

Ayala, Helon Vicente Hultmann; Segundo, Emerson H. V.; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1109/tmag.2015.2483060

Cited by 26 publications

(9 citation statements)

References 15 publications

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“…At the same time, the krill herd algorithm has good robustness and faster convergence by using group search, compared with other algorithms. Due to using the Lagrange model, the performance of the algorithm is better than other bionic optimization algorithms [27]. Similar to most intelligent optimization algorithms, the KH algorithm generally uses real-coded methods to generate initial populations randomly.…”

Section: Kh Algorithmmentioning

confidence: 99%

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Liu

et al. 2020

Sensors

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In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

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Section: Kh Algorithmmentioning

confidence: 99%

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Liu

et al. 2020

Sensors

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“…In the meantime, Swarm Intelligence (SI) is involved with an attractive figure in the area of applied electromagnetics. Numerous bio-inspired SI algorithms, such as Multi-Objective Particle Swarm Optimization (MOPSO) [7], non-dominated sorting genetic algorithm Version-II [12], bat algorithm [13] and it's variant Multi-Objective Bat Algorithm (MOBA) [9], krill herd optimizer [14] and it"s variant Multi-objective Krill Herd Optimizer (MOKHO) [14], Multi-Objective Grey Wolf Optimizer (MOGWO) [15], Multi-Objective Whale Optimization Algorithm (MOWOA) [16], Multi-Objective Moth Flame Optimizer (MOMFO) [17], predator-prey biogeography-based optimization [18], imperialist competitive optimizer [19] and it"s variant Multi-Objective modified imperialist competitive optimizer [19], pigeon-inspired optimizer [20], and it"s variant multi-objective pigeon-inspired optimizer [20], and sequential quadratic programming [21] have been directly applied to the design problem of BLDC motor. The optimal solutions are a group of non-dominated Pareto with the best trade-off between two or more cost functions placed on the Pareto front in multiobjective optimization problems (MOOPs).…”

Section: Introductionmentioning

confidence: 99%

MOCHIO: A Novel Multi-Objective Coronavirus Herd Immunity Optimization Algorithm for Solving Brushless Direct Current Wheel Motor Design Optimization Problem

KUMAR¹,

D²,

2021

Preprint

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A prominent and realistic problem in the domain of magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor such that it functions efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a novel optimization technique inspired by nature, called the Coronavirus Herd Immunity Optimizer (CHIO), is proposed. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of Coronavirus Herd Immunity Optimizer called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it has been directly used to optimize the BLDC motor design optimization problem. The BLDC motor design problem has two main objectives, such as minimization of the motor mass and maximization of the motor efficiency with five constraints and five design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. In addition, the experimental results are compared with the multi-objective whale optimization algorithm (MOWOA), multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimizer (MOPSO), multi-objective moth flame optimizer (MOMFO), and multi-objective bat algorithm (MOBA) are presented to confirm the viability and dominance of the proposed MOCHIO algorithm.

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“…The second classification covers the hybrid KH algorithms, and this includes the combination of cuckoo Search and KH [14], harmony search and KH [15], stud KH [16], biogeographybased KH [17], differential evolution based KH [18], etc. The third classification covers the variants of KH algorithms, and this includes discreet KH [19], binary KH [20], fuzzy KH [21], and multi-objective KH [22].…”

Section: Introductionmentioning

confidence: 99%

“…Many real-world applications of the KH algorithm exist in literature ranging from continuous optimization [23], combinatorial optimization [24], constrained optimization [25], multi-objective [26] and other related engineering domains [27]. Therefore, considering the relevance of the KH algorithm to the research communities, an effort is made in this paper to enhance its performance by means of improving its initialization schemes.…”

Section: Introductionmentioning

confidence: 99%

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

Agushaka

Ezugwu

2020

IEEE Access

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The krill herd (KH) algorithm is a global metaheuristic algorithm that was initially proposed for solving continuous optimization problems. The KH algorithm, since inception, has generated considerable real-world application interests in the research community. The standard algorithm solution implementation steps follow the initialization mechanism, which relies mainly on educated guesses or random initialization solution generation. Therefore, to improve the performance of the KH algorithm, the current study is set to investigate the influence of initializing the KH algorithm with three low-discrepancy sequences, such as the Faure sequence, Sobol sequence, and Van der Corput sequence. These low-discrepancy sequences are known to be more uniformly distributed across the problem search space than the commonly used random number initialization method. The study also evaluates the influence of population size on the performance of the proposed variants of the improved KH algorithms. The experimental results show significant improvements for the enhanced KH algorithms in terms of performance and the quality of solutions obtained; particularly on standard benchmarked high-dimension test problem instances, where the enhanced KH variants outperformed the existing basic KH algorithm for all the test functions evaluated. Similarly, the results for low dimension test cases showed less sensitivity to the initialization schemes, as the performance of our proposed improved scheme was comparable to that of the basic KH algorithm. However, in most cases, as the problem dimension was scaled up, the enhanced KH outperformed the basic KH. Evaluation results based on the population size of the algorithm, revealed that when the number of Krill is set at 25, the Sobol based KH initialization scheme performed better than did the other methods. Although, the Van der Corput and Faure based KH initialization schemes showed similar sensitivity when the dimension was set at 20. As we varied the population size of Krill, it was observed that the performance of the Sobol based KH initialization scheme deteriorated, whereas the other two methods showed superior performance. Overall, the findings from this study revealed that there are significant improvements in the performance of KH algorithm when initialized with low-discrepancy sequences.

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Multiobjective Krill Herd Algorithm for Electromagnetic Optimization

Cited by 26 publications

References 15 publications

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

MOCHIO: A Novel Multi-Objective Coronavirus Herd Immunity Optimization Algorithm for Solving Brushless Direct Current Wheel Motor Design Optimization Problem

Influence of Initializing Krill Herd Algorithm With Low-Discrepancy Sequences

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