Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm-Based Constrained Multi-Objective Nonlinear Planetary Gearbox Optimization

Abstract

The multi-objective optimization (MOO) of a planetary gearbox is a challenging optimization problem, which includes simultaneous minimization of a number of conflicting objectives including gearbox volume, contact ratio, power loss, etc., and at the same time satisfying a number of complex constraints. This paper addresses this complex problem by proposing a modified hybrid algorithm, named Multi-objective Hybrid Butterfly Optimization and Particle Swarm Optimization Algorithm (HMOBPSO), which integrates PSO and Particle Swarm Optimization (BOA) algorithms with the aim to improve the performance with respect to the considered problem. The proposed approach solves the non-convex Pareto set and provides vital insights for lowering gear weight and efficiency and avoiding early failure. The experimental analysis employs numerical simulations to determine the Pareto optimal solutions to the formulated MOO problem. The results show that the proposed method offers significant improvements in terms of gearbox size, efficiency, and spacing compared to the conventional methods. In addition, an assessment of the optimization performance of the proposed HMOBPSO algorithm has been conducted by comparing it to other established algorithms across several ZDT and DTLZ benchmark problems, where it demonstrated its effectiveness.

Keywords

• multi-objective optimization
• planetary gear trains
• gear efficiency
• Particle Swarm Optimization
• butterfly optimization algorithm