Institutional Repository
Technical University of Crete
EN  |  EL



My Space

A new hybrid Firefly – Genetic algorithm for the optimal product line design problem

Zervoudakis Konstantinos, Tsafarakis Stelios, Sovatzidi Paraskevi-Panagiota

Simple record

Extent14 pagesen
TitleA new hybrid Firefly – Genetic algorithm for the optimal product line design problemen
CreatorZervoudakis Konstantinosen
CreatorΖερβουδακης Κωνσταντινοςel
CreatorTsafarakis Steliosen
CreatorΤσαφαρακης Στελιοςel
CreatorSovatzidi Paraskevi-Panagiotaen
CreatorΣοβατζιδη Παρασκευη-Παναγιωταel
PublisherSpringer Natureen
Content SummaryThe 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.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Date of Item2022-05-17-
Date of Publication2019-
SubjectProduct line designen
SubjectFirefly algorithmen
SubjectGenetic algorithmen
Bibliographic CitationK. Zervoudakis, S. Tsafarakis, and P.-P, Sovatzidi, “A new hybrid Firefly – Genetic algorithm for the optimal product line design problem,” in Learning and Intelligent Optimization, vol 11968, Lecture Notes in Computer Science, N. Matsatsinis, Y. Marinakis, P. Pardalos, Eds., Cham, Switzerland: Springer Nature, 2020, pp. 284–297, doi: 10.1007/978-3-030-38629-0_23.en