Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

Lee, Kun Chou; Lu, Pai Ting

doi:10.1155/2020/8205797

Cited by 13 publications

(12 citation statements)

References 22 publications

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“…Existing research works [16][17][18][19][20][21][22][23][24][25] have exposed certain deficiencies of WOA viz. weak exploration, improper exploration-exploitation tradeoff, and insufficient solution diversity.…”

Section: Limitations Of the Standard Woa Approachmentioning

confidence: 99%

“…Thus, the WOA may become stuck in local optima, forcing premature convergence and causing weak solutions. Moreover, the exploration-exploitation imbalance also weakens the performance during initial phases because early stages require more exploration than exploitation [21].…”

Section: Limitations Of the Standard Woa Approachmentioning

confidence: 99%

“…When the current solution's fitness is equal to or better than the previous generation population's global best solution (Line 13), then WOA exploitation mechanism is applied on the current solution position to search nearby better solutions (Line 14-17). Line 18-25: When the current solution's fitness is less than the previous generation population's global best solution (Line 18), the h-DEWOA algorithm performs DE exploration activity for searching better far-away solutions (Line[19][20][21][22]. After that, OBL is applied on the real-value solution and select the best solution between the OBL solution and non-OBL solution (Line[23][24][25].…”

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confidence: 99%

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Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

et al. 2022

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Bag-of-Tasks (BoT) scheduling over cloud computing resources called Cloud Bag-of-Tasks Scheduling (CBS) problem, which is a well-known NP-hard optimization problem. Whale Optimization Algorithm (WOA) is an effective method for CBS problems, which still requires further improvement in exploration ability, solution diversity, convergence speed, and ensuring adequate exploration–exploitation tradeoff to produce superior scheduling solutions. In order to remove WOA limitations, a hybrid oppositional differential evolution-enabled WOA (called h-DEWOA) approach is introduced to tackle CBS problems to minimize workload makespan and energy consumption. The proposed h-DEWOA incorporates chaotic maps, opposition-based learning (OBL), differential evolution (DE), and a fitness-based balancing mechanism into the standard WOA method, resulting in enhanced exploration, faster convergence, and adequate exploration–exploitation tradeoff throughout the algorithm execution. Besides this, an efficient allocation heuristic is added to the h-DEWOA method to improve resource assignment. CEA-Curie and HPC2N real cloud workloads are used for performance evaluation of scheduling algorithms using the CloudSim simulator. Two series of experiments have been conducted for performance comparison: one with WOA-based heuristics and another with non-WOA-based metaheuristics. Experimental results of the first series of experiments reveal that the h-DEWOA approach results in makespan improvement in the range of 5.79–13.38% (for CEA-Curie workloads), 5.03–13.80% (for HPC2N workloads), and energy consumption in the range of 3.21–14.70% (for CEA-Curie workloads) and 10.84–19.30% (for HPC2N workloads) over well-known WOA-based metaheuristics. Similarly, h-DEWOA also resulted in significant performance in comparison with recent state-of-the-art non-WOA-based metaheuristics in the second series of experiments. Statistical tests and box plots also revealed the robustness of the proposed h-DEWOA algorithm.

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Section: Limitations Of the Standard Woa Approachmentioning

confidence: 99%

Section: Limitations Of the Standard Woa Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

et al. 2022

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“…In [15], bat algorithm (BA), as a natural heuristic optimization algorithm with excellent performance, was used to solve microwave imaging problems. In [16], the whale optimization algorithm (WOA) was applied to solve the inverse scattering problem, which was a problem solving the reconstruction of an imperfect conductor with corners.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Algorithm Based on a Modified Sine Cosine Algorithm and Least Square and Its Application to Microwave Imaging

Wang

2021

International Journal of Antennas and Propagation

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With the rapid development of materials science and medical imaging technology, traditional optimization algorithms cannot solve the problem of inverse scattering of complex scatterers well. Therefore, more and more imaging algorithms for solving complex scatterers were proposed. In this paper, a novel hybrid algorithm is put forward for the microwave imaging problem. First, the proposed algorithm improves the search path of the traditional sine cosine algorithm, which obtains better global search capability. Second, the least square is introduced to form judging and contrasting mechanisms, which forms the parallel algorithm simultaneously, in order to make the proposed algorithm more suitable for the diverse microwave imaging problem. To prove the efficiency of the proposed algorithm, several examples, including ten benchmark functions, two engineering design problems, and three different microwave imaging problem tests are adopted. As expected, the results show that the proposed algorithm achieves not only superior optimal value but also the reconstruction of the complicated permittivity of the scatterer compared with several traditional optimization algorithms.

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“…Currently, the microwave imaging algorithms commonly utilized are mainly divided into three categories. e first type is stochastic optimization algorithm, such as evolution strategy [4], particle swarm optimization [5], and whale optimization algorithm [6]. is type of algorithms converts the microwave imaging problem into an optimization problem and obtains the result by continuous iterating.…”

Section: Introductionmentioning

confidence: 99%

The Comparison of Performance between Contrast Source Inversion and Its Related Algorithms and Several Improved Methods

Wang

2021

International Journal of Antennas and Propagation

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The contrast source inversion (CSI) is an effective method for solving microwave imaging problems which is widely utilized. The core of the CSI is to change the conventional inverse scattering problem into an optimization problem. The two items in the objective function describe the state error and data error, respectively. As it is all known, there is almost no complete performance comparison based on Fresnel data for the CSI and its related improved algorithms. In addition, the performance of the algorithm under different weights was not analyzed before and the convergence speed of original CSI is slow. Firstly, this paper compares the performance of traditional CSI and its improved algorithms from three aspects of qualitative imaging effect, convergence speed, and objective function value based on Fresnel data. Secondly, the influence of the state error and the data error under different weights on the convergence rate and the objective function value are studied. For the limitation of a slower convergence rate, the CSI with weights (W-CSI), the CSI with dynamic reduction factor (CSI-DRF), and its related algorithms, which can get better convergence rate compared with their relative original algorithms, are proposed. Eventually, the future research work is prospected.

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Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

Cited by 13 publications

References 22 publications

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

A Hybrid Algorithm Based on a Modified Sine Cosine Algorithm and Least Square and Its Application to Microwave Imaging

The Comparison of Performance between Contrast Source Inversion and Its Related Algorithms and Several Improved Methods

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