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

被引：3

作者：

Jana, Sunanda ^{[1
]}

Dey, Anamika ^{[2
]}

Maji, Arnab Kumar ^{[3
]}

Pal, Rajat Kumar ^{[2
]}

机构：

[1] Haldia Inst Technol, Dept Comp Sci & Engn, Purba Medinipur 721657, W Bengal, India

[2] Univ Calcutta, Dept Comp Sci & Engn, JD-2,Sect 3, Kolkata 700106, W Bengal, India

[3] North Eastern Hill Univ, Dept Informat Technol, Shillong 793022, Meghalaya, India

来源：

INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING | 2021年 / 17卷 / 03期

关键词：

Chromosomes; Crossover; Female pheromones; Firefly mating algorithm; Fitness function; Genetic algorithm; Male flash brightness; Mating capability; Mutation; Population; Sudoku;

D O I：

10.1007/s11334-021-00397-4

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：261 / 275

页数：15

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