Abstract
Particle swarm optimization (PSO) has shown to be a robust and efficient optimization algorithm therefore PSO has received increased attention in many research fields. This paper demonstrates the feasibility of applying the Dynamic Inertia Weight Particle Swarm Optimization to solve a Non-Polynomial (NP) Complete puzzle. This paper presents a new approach to solve the Nonograms Puzzle using Dynamic Inertia Weight Particle Swarm Optimization (DIW-PSO). We propose the DIW-PSO to optimize a problem of finding a solution for Nonograms Puzzle. The experimental results demonstrate the suitability of DIW-PSO approach for solving Nonograms puzzles. The outcome results show that the proposed DIW-PSO approach is a good promising DIW-PSO for NP-Complete puzzles.
Highlights
Most of optimization problems including NP-complete problem, such as Nonograms puzzle, have complex characteristics with heavy constraints
Nonograms are deceptively simple logic puzzles, which is considered as an image reconstruction problem, starting with a blank N × M grid, Fig. 1.a shows an example for 5 x 5 Nonograms puzzle
We presented a new algorithm for solving Nonograms
Summary
Most of optimization problems including NP-complete problem, such as Nonograms puzzle, have complex characteristics with heavy constraints. The solution of the puzzle is an image grid that satisfies certain row and column constraints. The constraints take the form of series of numbers at the head of each line (row or column) indicating the size of blocks of contiguous filled cells found on that line. The puzzle solvers need to figure out which square will be left blank (white) and which will be colored (black), based on the numbers at the side of the grid. Sk numbers at the side of the row or column: indicated that there are groups of s1, s2, and sk filled squares, with at least one blank square between consecutive groups. A puzzle is complete when all rows and columns are filled, and meet their definitions, without any contradictions. We demonstrate that DIWPSO can be specified to NP-Complete puzzle
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