Clonal selection algorithms for optimal product line design: A comparative study

Product design constitutes a critical process for a firm to stay competitive. Whilst the biologically in-spired Clonal Selection Algorithms (CSA) have been applied to efficiently solve several combinatorial op-timization 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 hy -permutation 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 perfor-mance 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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study demonstrates the efficiency of biologically-inspired Clonal Selection Algorithms (CSA) in solving product line optimization problems and investigates the robustness of each method to different combinatorial sizes and objectives.