Solving Sudoku puzzles with node based Coincidence algorithm

Waiyapara, Kiatsopon; Wattanapornprom, Warin; Chongstitvatana, Prabhas

doi:10.1109/jcsse.2013.6567311

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…To illustrate the performance of LSGA, we compare it with state-of-the-art algorithms, including the node-based coincidence algorithm named NB-COIN [13], the preserve building blocks GA named GA-I [46], and the GA with local optima handling named GA-II [18]. To make a fair comparison, the population size is set to 150, while all algorithms run 1×10 4 generations.…”

Section: A Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…So far, the existing algorithms for solving Sudoku puzzles can be divided into mathematical algorithms [11] and heuristics algorithms [12]. The exact algorithms are faster at solving Sudoku puzzles, but lack portability [13]. Therefore, as a type of heuristics algorithm, GA has gained widespread attention due to its powerful search ability and versatility.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for Sudoku Puzzles

Wang

Sun

et al. 2024

IEEE Trans. Games

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Sudoku puzzles are not only popular intellectual games but also NP-hard combinatorial problems related to various real-world applications, which have attracted much attention worldwide. Although many efficient tools, such as evolutionary computation (EC) algorithms, have been proposed for solving Sudoku puzzles, they still face great challenges with regard to hard and large instances of Sudoku puzzles. Therefore, to efficiently solve Sudoku puzzles, this paper proposes a genetic algorithm (GA)-based method with a novel local search technology called local search-based GA (LSGA). The LSGA includes three novel design aspects. First, it adopts a matrix coding scheme to represent individuals and designs the corresponding crossover and mutation operations. Second, a novel local search strategy based on column search and sub-block search is proposed to increase the convergence speed of the GA. Third, an elite population learning mechanism is proposed to let the population evolve by learning the historical optimal solution. Based on the above technologies, LSGA can greatly improve the search ability for solving complex Sudoku puzzles. LSGA is compared with some state-of-the-art algorithms at Sudoku puzzles of different difficulty levels and the results show that LSGA performs well in terms of both convergence speed and success rates on the tested Sudoku puzzle instances.

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Section: A Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for Sudoku Puzzles

Wang

Sun

et al. 2024

IEEE Trans. Games

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“…In this paper, introduce applications of node-based estimation of distribution algorithms (EDA) [5] [6] for solving the order acceptance with capacity balancing problem. The contribution is to demonstrate a new approach to the order acceptance problem that competed successfully with previously purposed genetic algorithm (GA) especially in larger problems.…”

Section: Introductionmentioning

confidence: 99%

Application of node based estimation of distribution algorithms for solving order acceptance with capacity balancing problems by trading off between over capacity and under capacity utilization

Wattanapornprom

et al. 2014

2014 International Computer Science and Engineering Conference (ICSEC)

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Order acceptance with capacity balance problems require trading off between over and under capacity utilization in order to gain more profits. This research proposes a new over and under capacity tradeoff order acceptance model and proposes adaptations of node based estimation of distribution algorithms to solve the order acceptance decisions in multi-process environments. The results show that node based coincidence algorithm is a potential algorithm which can maximize both profit and can maximize the capacity utilization at the same time.Keywordsorder acceptance, capacity balancing, combinatorial optimization, estimation of distribution algorithm, genetic algorithm, node based coincidence algorithm, node histogram based sampling algorithm

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Minimizing Makespan Using Node Based Coincidence Algorithm in the Permutation Flowshop Scheduling Problem

Srimongkolkul

Chongstitvatana

2015

Lecture Notes in Electrical Engineering

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Solving Sudoku puzzles with node based Coincidence algorithm

Cited by 6 publications

References 9 publications

A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for Sudoku Puzzles

A Novel Evolutionary Algorithm With Column and Sub-Block Local Search for Sudoku Puzzles

Application of node based estimation of distribution algorithms for solving order acceptance with capacity balancing problems by trading off between over capacity and under capacity utilization

Minimizing Makespan Using Node Based Coincidence Algorithm in the Permutation Flowshop Scheduling Problem

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