Abstract
A novel approach to the problem of grid-based path finding has been introduced. The method is a block-based search algorithm, founded on the bases of two algorithms, namely the quad-tree algorithm, which offered a great opportunity for decreasing the time needed to compute the solution, and the harmony search (HS) algorithm, a meta-heuristic algorithm used to obtain the optimal solution. This quad HS algorithm uses the quad-tree decomposition of free space in the grid to mark the free areas and treat them as a single node, which greatly improves the execution. The results of the quad HS algorithm have been compared to other meta-heuristic algorithms, i.e., ant colony, genetic algorithm, particle swarm optimization and simulated annealing, and it was proved to obtain the best results in terms of time and giving the optimal path.
Highlights
Throughout the past few decades, the global interest of researchers has been in the grid-based graph representations and path-planning problems, since they have been shown to be of great significance for many practical applications and research studies
Analysing the simulation results provided by quad harmony search (QHS) algorithm, we can infer that at a lower percentage of obstacles (10 %-20 %) we mainly get faster planning times, because we use the QT algorithm to determine the free areas in the grid-based graph
Comparing the results we have obtained with QHS to ant colony (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and simulated annealing (SA), several crucial conclusions have been drawn
Summary
Throughout the past few decades, the global interest of researchers has been in the grid-based graph representations and path-planning problems, since they have been shown to be of great significance for many practical applications and research studies. Several of them were utilized in response to problems in the areas of computer vision, medical informatics, CAD/CAM-design, gaming and robotics. All these problems and applications become more challenging if they need to be solved in real-time. The algorithms employed for grid-based path finding can fall into two categories: deterministic and meta-heuristic. Some of the meta-heuristic algorithms employed to solve the path-finding problems are: tabu search (TS), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and simulated annealing (SA). It has not always been possible to find an optimal path in grid-based environments using such algorithms, these researches have served as a great
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