How to accurately and efficiently perform data clustering in complex multidimensional data analysis and processing tasks is a challenging research problem. Traditional optimizationalgorithms often need help with the ...
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How to accurately and efficiently perform data clustering in complex multidimensional data analysis and processing tasks is a challenging research problem. Traditional optimizationalgorithms often need help with the problems of quickly falling into local optimum and insufficient global search ability when dealing with high-dimensional, multi-peaked and complex structured data. In order to solve this challenge, a student psychology based optimization algorithm (GDLSPBO) that integrates differential evolution and hierarchical learning mechanisms was proposed, aiming to improve the accuracy, stability and global optimization ability of data clustering. GDLSPBO enhances the population diversity and prevents the algorithm from falling into local optimum by introducing the differential evolution mechanism. Simultaneously, the hierarchical learning strategy improves the algorithm's search efficiency and local optimization ability. The experiments are validated on the CEC-BC-2017 benchmark functions. Several real datasets and the results show that GDLSPBO achieves an F-measure of 0.9595 and an Adjusted Rand coefficient of 0.8578 on the Cancer dataset, and the clustering accuracy on the Iris dataset reaches 93.33%, which is significantly better than that of other classical optimizationalgorithms. This indicates that GDLSPBO has a more substantial clustering effect and higher solution accuracy in solving complex data clustering problems. The experimental results verify that the global search ability and optimization accuracy of GDLSPBO on multidimensional complex data sets have been significantly improved, demonstrating its broad applicability and robustness in practical data clustering applications.
This paper investigates the task scheduling problem for the Earth observation Interferometric Synthetic Aperture Radar (InSAR) satellite system. The mission time window generation method is introduced, and the constra...
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This paper investigates the task scheduling problem for the Earth observation Interferometric Synthetic Aperture Radar (InSAR) satellite system. The mission time window generation method is introduced, and the constraint satisfaction model for task scheduling in the InSAR satellite system is constructed. To address the mission allocation issue between the chief satellite and deputy satellites, a mission conflict detection and resolution mechanism is developed. Moreover, based on the single-objective studentpsychology-basedoptimization (SPBO) algorithm, a modified non-dominated sorting SPBO (NSSPBO) algorithm is proposed to tackle the multi-objective task scheduling problem for the InSAR satellite system. Numerical simulations are presented to demonstrate the effectiveness and superiority of the proposed NSSPBO algorithm.
To tackle the low convergence accuracy and vulnerability to local optima problems of the SPBO algorithm, a novel studentpsychologybasedoptimization (NSPBO) algorithm is introduced, integrating different enhancement...
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ISBN:
(纸本)9798350377859;9798350377842
To tackle the low convergence accuracy and vulnerability to local optima problems of the SPBO algorithm, a novel studentpsychologybasedoptimization (NSPBO) algorithm is introduced, integrating different enhancement strategies. Firstly, a wavelet mutation strategy is implemented to improve the guiding function of the optimal individual in the algorithm's search process, thus enhancing the algorithm's local optimization capability. Secondly, a weighted center and Cauchy mutation mechanism are integrated into the randomly enhanced student individuals to maintain diversity among student individuals and avoid the algorithm from getting stuck in local optima prematurely. Finally, a region-based learning search strategy is applied to the entire population to boost the algorithm's search accuracy and expedite the search process. Experimental tests were conducted on eighteen standard test functions to compare the performance of the proposed algorithm with other intelligent optimizationalgorithms. The findings indicate that the proposed algorithm exhibits exceptional optimization performance, excelling in both accuracy and convergence speed.
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