Scholarlink

<p>Metaheuristic algorithms constitute one of the most efficient methods for addressing the Agile Earth Observation Satellite Scheduling Problem (AEOSSP). Hence, exploring the capability of a new metaheuristic algorithm called Equilibrium Optimizer (EO) in this domain, is one of the key contributions of this paper. In this work we aim to analyzes the limitations of the original EO algorithm in addressing the specific requirements of AEOSSP. Specifically, the integer-point data representation within the algorithm population, emerges as a challenge for this subject. In response, a new variant, Stratified Sampling Equilibrium Optimizer (SSEO) is proposed to address these deficiencies. Taking into account the specificity of this field, targeted improvements have been introduced. First, the EO algorithm was adapted to support integer-point representation, ensuring compatibility with the discrete encoding scheme. Second, a stratified sampling mechanism is incorporated to mitigate variable duplication across particles, a critical requirement since the AEOSSP formulation strictly prohibits task repetition within a single schedule. Finally, a stagnation mitigation strategy is introduced, leveraging a tailored Lévy flight mechanism. This mechanism includes a customized Lévy step generation method, designed specifically to align with the characteristics of the AEOSSP. The proposed SSEO algorithm is validated using real-world satellite data and demonstrates superior performance across multiple aspects, including solution quality (fitness), computational efficiency (processing time), and robustness against local optima. The implementation code for the proposed Stratified Sampling Equilibrium Optimizer (SSEO) algorithm is publicly available at: <a href="https://github.com/EnimaG/SSEO">https://github.com/EnimaG/SSEO</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Stratified sampled equilibrium optimizer for agile earth observation satellite observation tasks scheduling

  • 期刊论文

  • 发表日期：

    2025年11月28日

查看全文
  • Mohamed Elamine Galloua,
  • Shuai Li,
  • Jiahao Cui

摘要

Metaheuristic algorithms constitute one of the most efficient methods for addressing the Agile Earth Observation Satellite Scheduling Problem (AEOSSP). Hence, exploring the capability of a new metaheuristic algorithm called Equilibrium Optimizer (EO) in this domain, is one of the key contributions of this paper. In this work we aim to analyzes the limitations of the original EO algorithm in addressing the specific requirements of AEOSSP. Specifically, the integer-point data representation within the algorithm population, emerges as a challenge for this subject. In response, a new variant, Stratified Sampling Equilibrium Optimizer (SSEO) is proposed to address these deficiencies. Taking into account the specificity of this field, targeted improvements have been introduced. First, the EO algorithm was adapted to support integer-point representation, ensuring compatibility with the discrete encoding scheme. Second, a stratified sampling mechanism is incorporated to mitigate variable duplication across particles, a critical requirement since the AEOSSP formulation strictly prohibits task repetition within a single schedule. Finally, a stagnation mitigation strategy is introduced, leveraging a tailored Lévy flight mechanism. This mechanism includes a customized Lévy step generation method, designed specifically to align with the characteristics of the AEOSSP. The proposed SSEO algorithm is validated using real-world satellite data and demonstrates superior performance across multiple aspects, including solution quality (fitness), computational efficiency (processing time), and robustness against local optima. The implementation code for the proposed Stratified Sampling Equilibrium Optimizer (SSEO) algorithm is publicly available at: https://github.com/EnimaG/SSEO.