A novel oppositional teaching learning strategy based on the golden ratio to solve the Time-Cost-Environmental impact Trade-Off optimization problems

Eirgash M. A., TOĞAN V.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.224, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 224
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.eswa.2023.119995
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Public Affairs Index, Civil Engineering Abstracts
  • Anahtar Kelimeler: Multi -objective optimization, Opposition based -learning, Golden ratio, Pareto-front, Environmental impact, Construction project, DECISION-MAKING MODEL, CONSTRUCTION PROJECTS, MULTIOBJECTIVE OPTIMIZATION, PERFORMANCE, DESIGN
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The teaching-learning-based optimization (TLBO) algorithm is one of the most noted meta-heuristic algorithms known for its simplicity, fast convergence, and accuracy. Several TLBO variants have been suggested to improve the exploitation capability and speed up the exploration process. However, they still suffer from slow conver-gence and local optimum stagnation when solving sophisticated time-cost-environmental impact trade-off (TCET) problems. To tackle this, the study proposes a new brand of TLBO named "golden ratio oppositional TLBO" (GROTLBO), which leverages a novel golden ratio-based opposition learning (GROL) strategy to eliminate premature convergence. The proposed model, instead of employing opposite numbers, employs golden ratio -based opposite numbers for opposition-based population initialization and generation jumping to enrich the diversity and local optimal avoidance. The asymmetric and iteratively changing search space remarkably en-hances the probability of the population achieving the optimal solution, improving the overall exploitation and exploration abilities of GROTLBO. To evaluate its performance, three benchmark problems consisting of 11, 25, and 69-activity projects are examined. The computational experiment results compared with the competitive algorithms show that GROTLBO has a 5% improvement in accuracy. Additionally, GROTLBO was able to considerably reduce the number of scheduling calculations required. These findings suggest that GROTLBO is a promising optimization algorithm that provides robust and high-quality Pareto-optimal solutions in the objective space of the hyper-volume indicator.