A new hybrid Firefly – Genetic algorithm for the optimal product line design problemA new hybrid Firefly – Genetic algorithm for the optimal product line design problem Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2022-05-172019enThis research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (ΙΚΥ).The optimal product line design is one of the most critical decisions for a firm to stay competitive, since it is related to the sustainability and profitability of a company. It is classified as an NP-hard problem since no algorithm can certify in polynomial time that the optimum it identifies is the overall optimum of the problem. The focus of this research is to propose a new hybrid optimization method (FAGA) combining Firefly algorithm (FA) and Genetic algorithm (GA). The proposed hybrid method is applied to the product line design problem and its performance is compared to those of previous approaches, like genetic algorithm (GA) and simulated annealing (SA), by using both actual and artificial consumer-related data preferences for specific products. The comparison results demonstrate that the proposed hybrid method is superior to both genetic algorithm and simulated annealing in terms of accuracy, efficiency and convergence speed.http://creativecommons.org/licenses/by/4.0/284–29713th International Conference on Learning and Intelligent OptimizationLearning and Intelligent Optimization Zervoudakis Konstantinos Ζερβουδακης Κωνσταντινος Tsafarakis Stelios Τσαφαρακης Στελιος Sovatzidi Paraskevi-Panagiota Σοβατζιδη Παρασκευη-Παναγιωτα Springer Nature Product line design Hybridization Firefly algorithm Genetic algorithm