Research on Robot Path Perception and Optimization Technology Based on Whale Optimization Algorithm

Zan, Jie

doi:10.47852/bonviewjcce597820205514

Cited by 54 publications

(17 citation statements)

References 18 publications

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“…Currently, many fields are facing highly challenging optimization problems that must simultaneously meet multiple conflicting objectives. Therefore, for multi-objective optimization (MOO) problems, it is necessary to find a set of solutions that can meet multi-objective equilibrium [ 1 ]. For MOO, in recent years, evolutionary algorithm has been gradually applied to MOO problems due to its strong practicability and high learning rate.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization based doherty power amplifier and its matching network optimization method

Sun

2023

PLoS ONE

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In the actual design process of traditional power amplifiers, there is a problem of being cumbersome and unable to simultaneously meet low power and saturation modes. Therefore, an improved multi-objective optimization algorithm proposed by decomposition is introduced to optimize its matching network to achieve overall optimization design of power amplifiers. The algorithm, matching network, and optimized power amplifier performance are simulated and verified. The experimental outcomes denote that on the logic function with Zener diode transistor, the proposed algorithm has a mean generation distance index of 5.03E-3, which is lower than most algorithms. Its overall comprehensive performance is better than the comparison algorithm, and compared to the comparison algorithm, it converges more quickly in the early stage of iteration on 1 and 2, and tends to stabilize in the 40th generation, and completes convergence in the 80th generation. In addition, the optimal solution has already begun to appear around the 25th generation and reached saturation around the 70th generation. At the same time, in the actual working bandwidth, the optimized power amplifier saturation efficiency reaches 51.5%~61.9%, and the efficiency at 6dB power backoff is about 44.4%~56.5%. Overall, the algorithm proposed in the study is effective in optimizing power amplifiers and their matching networks, effectively solving the problem of insufficient efficiency in low power modes in traditional designs.

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Section: Introductionmentioning

confidence: 99%

A multi-objective optimization based doherty power amplifier and its matching network optimization method

Sun

2023

PLoS ONE

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“…In the coming era, the ANN-GA framework is a better alternative to investigate for providing outstanding output from difficult nonlinear issues involving fractional analysis [49], three-point second-order boundary value problems analysis [50], Bouc-Wen hysteresis model [51], nonlinear Emden-Fowler equation [52], and nanofluidic problems [53]. ANN-GA-SQP can also be used in Face Intelligent Perception Technology Integrating Deep Learning under Different Illumination Intensities [54], Vehicle Automatic Driving Target Perception Technology Based on Improved MSRPN Algorithm [55], Research on Robot Path Perception and Optimization Technology based on whale optimization algorithm [56], fractional COVID-19 model [57], risk factors in HIV Transmission: [58], model of immobilized enzyme [59] and deep learning applications [60].The solver's precision and efficiency will be improved using statistical heuristics techniques combined with local search methodology.…”

Section: Discussionmentioning

confidence: 99%

Integrated Neuro-Evolution-Based Computing Paradigm to Study the COVID-19 Transposition and Severity in Romania and Pakistan

Shoaib

Abukhaled

Kainat

et al. 2022

Int J Comput Intell Syst

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Numerical treatment of the COVID-19 transposition and severity in Romania and Pakistan has been presented in this study, i.e., ANN-GA-SQP through artificial neural network genetic algorithms (ANN-GA) and sequential quadratic programming (SQP), a design of an integrated computational intelligent paradigm, COVID-19 is widely considered to be the greatest health threat humanity has ever faced. In terms of both health and economics, COVID-19 is a huge disaster. Many academics have looked at the COVID-19 model in their research papers, although they use different traditional techniques to represent it. The use of hybrid suggested solutions to solve this issue in the present article is significant, demonstrating the study's novelty. The SIR model of COVID-19 consists of a susceptible, infectious, and recovered class of population. The activation function for the construction of functions based on fitness in mean squared error sense is developed using nonlinear equations of the COVID-19 SIR model for the best performance of ANN-GA-SQP with the combined potential of GA and SQP of a network. While detailed refining is done with efficient local search with SQP, GAs operates as a global search. In addition, a neuron analysis will be presented to verify the effectiveness and complexity of the proposed method. Adam’s numerical methodology is applied to compare the sustainability and efficacy of the presented paradigm. Analytical evaluations of mean, median, and semi-interquartile range values, as well as Theil’s inequality coefficients, root mean squared error, and mean of absolute deviation) values have been observed. The convergence and correctness of the ANN-GA-SQP approach are further validated by statistical analyses.

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“…In Table 5 , by adjusting the algorithm parameters, the trends of different indicators can be observed [ 27 ]. Function set expansion may lead to over-fitting, resulting in a decrease in accuracy.…”

Section: Fs Algorithm Based On Optimized Ga and The Application In Hi...mentioning

confidence: 99%

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feng

2024

PLoS ONE

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High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve is the best, with stable performance and a time range of 0–300. While maintaining the efficiency, it can reach 4.48 as soon as possible. The feature selection method has practical significance for high-dimensional data processing, improves the efficiency and accuracy of data processing, and provides an effective new method for high-dimensional data processing.

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Research on Robot Path Perception and Optimization Technology Based on Whale Optimization Algorithm

Cited by 54 publications

References 18 publications

A multi-objective optimization based doherty power amplifier and its matching network optimization method

A multi-objective optimization based doherty power amplifier and its matching network optimization method

Integrated Neuro-Evolution-Based Computing Paradigm to Study the COVID-19 Transposition and Severity in Romania and Pakistan

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

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