In reality, there are many extremely complex nonlinear optimization problems. How to locate the roots of nonlinear equation systems (NESs) more accurately and efficiently has always been a major numerical challenge. A...
详细信息
In reality, there are many extremely complex nonlinear optimization problems. How to locate the roots of nonlinear equation systems (NESs) more accurately and efficiently has always been a major numerical challenge. Although there are many excellent algorithms to solve NESs, which are all limited by the fact that the algorithm can solve at most one NES in a single run. Therefore, this paper proposes a historical knowledge transfer driven self-adaptive evolutionary multitasking algorithm framework (EMSaRNES) with hybrid resource release to solve NESs. Its core is that in one run, EMSaRNES can efficiently and accurately locate the roots of multiple NESs. In EMSaRNES, self-adaptive parameter method is proposed to dynamically adjust parameters of the algorithm. Secondly, adaptive selection mutation mechanism with historical knowledge transfer is designed, which dynamically adjusts the evolution of populations with or without knowledge sharing according to changes in the current population diversity, thereby balancing population diversity and convergence. Finally, hybrid resource release strategy is developed, which archives the roots that meet the accuracy requirements, and then three distributions are selected to generate new populations, thus ensuring that the population diversity is maintained at high level. After a variety of experiments, it has been proven that compared to comparative algorithms EMSaRNES has superior performance on 30 general NESs test sets. In addition, the results on 18 extremely complex NESs test sets and two real-life application problems further prove that EMSaRNES finds more roots in the face of complex problems and real-life problems.
Band selection (BS) is a critical method for hyperspectral image (HSI) classification, which helps to reduce the computational burden while providing a good class separability. However, most BS methods only deal with ...
详细信息
Band selection (BS) is a critical method for hyperspectral image (HSI) classification, which helps to reduce the computational burden while providing a good class separability. However, most BS methods only deal with each HSI dataset independently, which cannot effectively exploit useful knowledge across multiple datasets. Only one BS method tries to transfer knowledge across multiple datasets, but it ignores the negative transfer effect caused by the inherent difference among datasets. To alleviate this issue, this paper proposes an evolutionarymultitasking BS method with adaptive cross-dataset knowledge transfer. Specifically, a new knowledge transfer strategy is designed based on the linear mapping to filter out irrelevant knowledge among different datasets. Then, an adaptive transfer control strategy is designed to adaptively adjust the frequency and intensity of knowledge transfer, which can further enhance the knowledge transfer performance. As validated by our experimental studies, our method can properly mitigate the negative transfer effect caused by the differences in cross-dataset knowledge transfer. When compared to several state-of-the-art BS methods on three common HSI datasets, our method can find superior band subsets with higher quality.
With the rapid development of information technology making the scale of the Internet increasing day by day, collaborative optimization of multiple scheduling tasks in a multi-cloud environment provides users with fas...
详细信息
ISBN:
(纸本)9789819722716;9789819722723
With the rapid development of information technology making the scale of the Internet increasing day by day, collaborative optimization of multiple scheduling tasks in a multi-cloud environment provides users with faster scheduling options. Meanwhile, there is a certain similarity between cloud scheduling tasks, and in order not to waste the similarity between tasks, similar tasks are linked together to find an optimal scheduling solution for multiple tasks, making it possible to handle multiple scheduling tasks simultaneously. Firstly, we construct a multi-objective optimization model considering time, cost and VM resource load balance;secondly, since there are not only independent optimization problems in real scenarios, we adapt the constructed multiple similar optimization models and propose a multi-task multi-objective optimization model;finally, to be able to solve the constructed model better, we use a proposed objective function-based Finally, we propose an evolutionary multitasking algorithm based on weighted summation of the objective functions, which allows the algorithm to find the optimal solution among multiple multi-objective models. Simulation experiments show that the proposed algorithm has better performance.
暂无评论