Dynamic multi-objective evolutionary algorithms in noisy environments

Sahmoud S., TOPCUOĞLU H. R.

Information Sciences, cilt.634, ss.650-664, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 634
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.ins.2023.03.094
  • Dergi Adı: Information Sciences
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Computer & Applied Sciences, INSPEC, Library, Information Science & Technology Abstracts (LISTA), Metadex, MLA - Modern Language Association Database, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.650-664
  • Anahtar Kelimeler: Change detection, Dynamic multi-objective optimization problems, Noise detection, Noisy optimization problems, Uncertainty, GENETIC ALGORITHMS, OPTIMIZATION
  • Marmara Üniversitesi Adresli: Evet

Özet

Real-world multi-objective optimization problems encounter different types of uncertainty that may affect the quality of solutions. One common type is the stochastic noise that contaminates the objective functions. Another type of uncertainty is the different forms of dynamism including changes in the objective functions. Although related work in the literature targets only a single type, in this paper, we study Dynamic Multi-objective Optimization problems (DMOPs) contaminated with stochastic noises by dealing with the two types of uncertainty simultaneously. In such problems, handling uncertainty becomes a critical issue since the evolutionary process should be able to distinguish between changes that come from noise and real environmental changes that resulted from different forms of dynamism. To study both noisy and dynamic environments, we propose a flexible mechanism to incorporate noise into the DMOPs. Two novel techniques called Multi-Sensor Detection Mechanism (MSD) and Welford-Based Detection Mechanism (WBD) are proposed to differentiate between real change points and noise points. The proposed techniques are incorporated into a set of Dynamic Multi-objective Evolutionary Algorithms (DMOEAs) to analyze their impact. Our empirical study reveals the effectiveness of the proposed techniques for isolating noise from real dynamic changes and diminishing the noise effect on performance.