A Cooperative Co-evolutionary Approach for Multi-objective Optimization

Basu, Sharbari; Mondal, Ankur; Basu, Abhishek

doi:10.1007/978-981-10-8863-6_7

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The mogDG-shift was combined with MOEA/D and NSGA-II, achieving better performance. MOEA/D (s & ns) [21] decomposed decision variables into two basic groups based on their separability and inseparability characteristics and then judged whether to divide each group based on population size. Zhang [22] proposed LMEA by decomposing decision variables into convergence-related variables and diversity-related variables based on their control information.…”

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

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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.

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Section: Introductionmentioning

confidence: 99%