Enhancing hybrid genetic algorithm performance in reducing steel usage for shipbuilding through sensitivity analysis

In ship construction, material costs constitute a substantial portion of the overall expenses. With the surging steel prices, the shipbuilding industry faces a pressing challenge. To counterbalance this issue, optimizing the structural components of ships has emerged as a viable solution. Genetic Algorithm (GA) methods, known for their application in structural optimization, have demonstrated their potential. However, the protracted computational time associated with GA remains a limiting factor. This research introduces a novel approach by merging GA with Finite Element Method (FEM) for optimizing plate sizes, resulting in a hybrid GA system. Moreover, the study incorporates sensitivity analysis (SA) due to its proven efficacy in enhancing optimization processes involving multiple variables. The SA component investigates plate interrelationships and effectively clusters them. After this grouping, the hybrid GA executes parallel optimization of plates that influence each other under tension. By integrating SA, the optimization process becomes faster and more time-efficient, while preserving optimal manufacturing costs. Remarkably, this methodology culminates in a substantial reduction in computational time when contrasted with the hybrid GA approach devoid of SA, all the while maintaining a parallel manufacturing cost trajectory. In conclusion, this study presents an innovative hybrid GA approach, supplemented with SA, as an effective strategy for mitigating the escalating steel costs in shipbuilding. The amalgamation of GA, FEM and SA synergistically simplifies the optimization process, ensuring optimal results in a faster way.