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A novel hybrid genetic algorithm-based firefly mating algorithm for solving Sudoku

التفاصيل البيبلوغرافية
العنوان: A novel hybrid genetic algorithm-based firefly mating algorithm for solving Sudoku
المؤلفون: Rajat Kumar Pal, Arnab Kumar Maji, Sunanda Jana, Anamika Dey
المصدر: Innovations in Systems and Software Engineering. 17:261-275
بيانات النشر: Springer Science and Business Media LLC, 2021.
سنة النشر: 2021
مصطلحات موضوعية: education.field_of_study, Steganography, Computer science, Population, Mathematical puzzle, Visual cryptography, law.invention, Local optimum, DNA computing, law, Genetic algorithm, Heuristics, education, Algorithm, Software
الوصف: Sudoku is an NP-complete-based mathematical puzzle, which has enormous applications in the domains of steganography, visual cryptography, DNA computing, and so on. Therefore, solving Sudoku effectively can bring revolution in various fields. Several heuristics are there to solve this interesting structure. One of the heuristics, genetic algorithm, is used by many researchers to solve Sudoku successfully, but they face various problems. Genetic algorithm has so many lacunas, and to overcome these, we have hybridised it in a novel way. In this paper, we have developed a hybrid genetic algorithm-based firefly mating algorithm, which can solve Sudoku instances with a greater success rate for easy, medium, and hard difficulty level puzzles. Our proposed method has controlled “getting stuck in local optima”, considering a smaller population and lesser generation.
تدمد: 1614-5054
1614-5046
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::100b1350f6e98549c4419ec0cbc0f24c
https://doi.org/10.1007/s11334-021-00397-4
حقوق: CLOSED
رقم الأكسشن: edsair.doi...........100b1350f6e98549c4419ec0cbc0f24c
قاعدة البيانات: OpenAIRE
الوصف
تدمد:16145054
16145046