Large Scale Many-Objective Optimization Driven by Distributional Adversarial Networks

Liang, Zhao; Li, Yunfan; Wan, Zhongwei

doi:10.48550/arxiv.2003.07013

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two kinds of direction vectors were used to generate convergence-related offspring and diversity-related offspring, respectively. Liang [35] and He [36] used distributional adversarial networks (DANs) and generative adversarial networks (GANs) instead of evolutionary operators to generate offspring, respectively. MOEAs based on novel reproduction operators design new evolutionary operators that directly act on large-scale decision variables to generate offspring.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping

Chen,

Shen,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems.

show abstract

Section: Introductionmentioning

confidence: 99%