A multi-objective Grey Wolf–Cuckoo Search algorithm applied to spatial truss design optimization - Vo, Nam; Tang, Huy; Lee, Jaehong (Applied soft computing, 2024)
A novel hybrid algorithm called Multi-Objective Hybrid Grey Wolf Cuckoo Search (MOGWOCS) is developed for spatial truss designs in this study. A new simple yet efficient mechanism to select the best candidates is proposed. Furthermore, harmonic averaging is employed to be a replacement for conventional arithmetic mean for higher effectiveness. Additionally, the Lévy flight in Cuckoo Search (CS) is utilized to increase efficiency in early searching and also reduce local entrapment possibility. For verification purposes, MOGWOCS is first performed on some mathematical functions and 11 CEC2020 mechanical problems. It is then examined on four large-scale truss design problems, in two of which multi-objective optimization is studied for the first time. To demonstrate the superiority of the proposed approach, five up-to-date algorithms, and various indicators are included for validation. It is found that MOGWOCS is able to produce solutions with higher optimality in terms of diversity and accuracy. •A novel hybridization of GWO and CS for multi-objective optimization is proposed.•Lévy flight and Harmonic averaging are exploited to enhance performance.•A new efficient mechanism to select the best candidates is introduced.•11 CEC2020 mechanical problems and four spatial truss structures are examined.•The proposed algorithm produces solutions with better diversity and accuracy.