Product design constitutes a critical process for a firm to stay competitive. Whilst the biologically inspired Clonal Selection Algorithms (CSA) have been applied to efficiently solve several combinatorial optimization problems, they have not yet been tested for optimal product lines. By adopting a previous comparative analysis with real and simulated conjoint data, we adapt and compare in this context 23 CSA variants. Our comparison demonstrates the efficiency of specific cloning, selection and somatic hypermutation operators against other optimization algorithms, such as Simulated Annealing and Genetic Algorithm. To further investigate the robustness of each method to combinatorial size, we extend the previous paradigm to larger product lines and different optimization objectives. The consequent performance variation elucidates how each operator shifts the search focus of CSAs. Collectively, our study demonstrates the importance of a fine balance between global and local search in such combinatorial problems, and the ability of CSAs to achieve it.

产品设计是公司保持竞争力的关键过程。虽然受生物学启发的克隆选择算法 (CSA) 已被应用于有效解决多个组合优化问题，但它们尚未针对最佳产品线进行测试。通过采用先前与真实和模拟联合数据的比较分析，我们在这种情况下调整和比较了 23 个 CSA 变体。我们的比较证明了特定克隆、选择和体细胞超变异算子与其他优化算法（例如模拟退火和遗传算法）的效率。为了进一步研究每种方法对组合大小的稳健性，我们将先前的范式扩展到更大的产品线和不同的优化目标。随之而来的性能变化阐明了每个操作员如何转移 CSA 的搜索重点。总的来说，我们的研究证明了在此类组合问题中实现全局和局部搜索之间良好平衡的重要性，以及 CSA 实现这一目标的能力。