A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

Wang, Ting; Xia, Kewen; Tang, Hailin; Zhang, Suwei; Sandrine, M

doi:10.1155/2020/9483971

Cited by 6 publications

(7 citation statements)

References 22 publications

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“…However, there is a problem that the wolf pack algorithm is not easy to break away the local solution, so that the cluster center point may not be optimal. In the three search stages of the implementation of the wolf pack algorithm, there are problems such as slow convergence, easy to fall into the local optimum, and unsatisfactory interaction of artificial wolves [13]. Therefore, this paper proposes the improved wolf pack algorithm based on the search strategy.…”

Section: K-means Algorithm Based On Improved Wpamentioning

confidence: 99%

Complaint electricity customer clustering method based on electricity big data

Zhang

Feng

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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In the fierce domestic market competition, the improvement of service quality is an important goal of electricity companies. This paper proposes the k-means algorithm based on the improved wolf pack algorithm, which is suitable for the power company complains, there are many factors that affect user complaints. Firstly, the PCA method is used to reduce the dimension of the complaint user’s behavior influencing factors, so as to improve the accuracy of clustering. Secondly, due to the shortcomings of falling into local optimal solution and low clustering accuracy, this paper proposes an improved wolf pack algorithm, which proposes the interactive strategy of walking behavior, calling behavior and an adaptive siege strategy for behavior, the clustering accuracy and convergence speed are improved.This algorithm overcomes the shortcomings of the original algorithm. Moreover, it improves the efficiency and clustering quality of the algorithm and it can realize the accurate classification of users.

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Section: K-means Algorithm Based On Improved Wpamentioning

confidence: 99%

Complaint electricity customer clustering method based on electricity big data

Zhang

Feng

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…A multi-constraint optimization problem is formulated for the sparse LAA to reduce the effect of mutual coupling between the element and keep a narrow main lobe width [10]. In this paper, the multi-constraint synthesis problem imposes a constraint on the minimum inter-element spacing, aperture length with null placement.…”

Section: (4) Case 4: Minimization Of the Peak Sll With Constraint On The Minimum Inter-element Spacing Aperture Length And Null Controlmentioning

confidence: 99%

“…It abases the performance of the designed antenna array [8]. However, attempts are been made by researchers to reduce the effect of mutual coupling among the array elements by altering the element position and aperture length [9] [10]. A few researchers have also employed the idea of reducing mutual coupling by minimizing the driving admittance of each identical array element and including this concept in their cost function definition [8] [11].…”

Section: Introductionmentioning

confidence: 99%

“…Several evolutionary algorithms have been successfully used to optimize antenna array but yet there is a need for better performance; thus, past algorithms are being improved on and new ones are also developed. Here are some of the intelligent optimization algorithms that have been used for antenna array synthesis: genetic algorithms (GA) [12] [13] [14], particle swarm optimization (PSO) algorithms [15] [16], quantum particle swarm optimization (QPSO) [17], ant colony optimization (ACO) [18], selfadaptive differential evolution (SADE) [19], backtracking search optimization algorithm (BSA) [20], symbiotic organisms search (SOS) [21], compressed sensing (CS) [22], biogeography-based optimization (BBO) [23], firefly algorithm (FA) [24] [25], grey wolf optimization (GWO) [9], moth flame optimization (MFO) [26], modified wolf pack Algorithm (MWPA) [10], hybrid particle swarm optimization (PSO) and convex (CVX) optimization (PSO-CVX) [27], convex optimization [28] and several others. These algorithms have been applied on different type of antennas array which includes linear [7] [25] [21] [19] [22] [9] [26] [22], circular [29] [26], cylindrical [30], conformal [31] [32] [33], hexagonal arrays [34], time-modulated array [27] [28], and lot more.…”

Section: Introductionmentioning

confidence: 99%

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Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

et al. 2021

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Pattern synthesis is a significant research focus in smart antennas due to its extensive use in several radar and communication systems. To improve the optimization performance of pattern synthesis of uniform and sparse linear antenna array, this paper presents an optimization method for solving the antenna array synthesis problem using the Mayfly Algorithm (MA). MA is a new heuristic algorithm inspired by the flight behavior as well as the mating process of mayflies, it has a unique velocity update system with great convergence. In this work, the MA was applied to linear antenna arrays (LAA) for optimal pattern synthesis in the following ways: by optimizing the antenna current amplitudes while maintaining uniform spacing and by optimizing the antenna positions while assuming a uniform excitation. Constraints of inter-element spacing and aperture length are imposed in the synthesis of sparse LAA. Sidelobe level (SLL) suppression with the placement of nulls in the specified directions is also implemented. The results gotten from this novel algorithm are validated by benchmarking with results obtained using other intelligent algorithms. In the synthesis of uniform 20-element LAA with nulls, MA achieved an SLL of -31.27 dB and the deepest null of -101.60 dB. Also, for sparse 20-element LAA, an SLL of -18.85 dB was attained alongside the deepest null of -87.37 dB. MA obtained an SLL of -35.73 dB and -23.68 dB for the synthesis of uniform and sparse 32-element LAA respectively. Finally, electromagnetism simulations are conducted using ANSYS Electromagnetics (HFSS) software, to evaluate the performance of MA for the beam pattern optimizations, taking into consideration the mutual coupling effects. The results prove that optimization of LAA using MA provides considerable enhancements in peak SLL suppression, null control, and convergence rate with respect to the uniform array and the synthesis obtained from other existing optimization techniques.

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“…Artificial bee colony algorithm (ABC) is a heuristic search technology based on swarm intelligence, which can mimic the foraging behavior of bees. Due to its advantages of few control parameters and strong global search ability, it is widely used in many fields, such as training neural networks [17], data clustering [18], business travel problems [19], and engineering optimization [20].…”

Section: Introductionmentioning

confidence: 99%

Design on Universal Circuit Breaker via Improved Gray Wolf Optimization Algorithm

Shui-dong

Xia

Shi

et al. 2020

Mathematical Problems in Engineering

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Miniaturization design of the universal circuit breaker is very necessary, but it is not enough to consider only the miniaturization in the design but also consider the energy consumption and breaking capacity of the universal circuit breaker. To this end, a comprehensive optimization design method in this paper is proposed and studied. Firstly, based on the analysis of the universal circuit breaker miniaturization model, combines with the universal circuit breaker’s low energy consumption model and high-segmentation model, a comprehensive optimization model for designing universal circuit breakers is constructed. Secondly, for the comprehensive model solution, an improved gray wolf optimization (GWO) algorithm is proposed, that is, a “cloud model” is introduced to balance the local search and global search capabilities to improve the convergence speed; also, a weight strategy is introduced to avoid falling into the local minimum, and simulations of typical test functions show that the improved algorithm is superior to other algorithms. Finally, the improved gray wolf optimization algorithm is applied to the comprehensive optimization design of universal circuit breakers. The experimental results show that the proposed comprehensive design method is feasible and improves the design accuracy and efficiency of the universal circuit breaker.

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A Modified Wolf Pack Algorithm for Multiconstrained Sparse Linear Array Synthesis

Cited by 6 publications

References 22 publications

Complaint electricity customer clustering method based on electricity big data

Complaint electricity customer clustering method based on electricity big data

Pattern Synthesis of Uniform and Sparse Linear Antenna Array Using Mayfly Algorithm

Design on Universal Circuit Breaker via Improved Gray Wolf Optimization Algorithm

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