Multi-objective design optimization for a two-stage transmission system under heavy load condition

A gear reducer is of critical importance to the performance of many transmission systems in automotive, marine, and aerospace applications. In the preliminary design phase, the pursuits of minimum volume, maximum surface fatigue life and maximum load capacity essentially becomes a multi-objective optimization problem. In this paper, a novel metaheuristics algorithm, Crow Search Algorithm (CSA), is extended to a multi-objective decision scenario based on the strategy embedded in the preference-inspired co-evolutionary algorithm using weights (PICEA-w). The proposed algorithm combines the nature of diversity, the rapid convergence of CSA and the efficient adaptive weights from PICEA-w. The genetic operator applied in the original PICEA-w aims to generate the offspring that is replaced by the food survey strategy of CSA with augmented efficiency. A hoisting transmission optimization problem from heavy cable shovel excavator is solved. Comparisons with other methods are provided to illustrate the effectiveness of the proposed optimization algorithm.