A novel approach to the problem of grid-based path finding has been introduced. The method is a blockbased 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 metaheuristic 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.
The area coverage is a popular problem in wireless sensor networks and many attempts in the past several years have been made to provide an optimal solution to this particular type of problem. This paper presents a novel approach to solving the area coverage problem, namely the Harmony Search optimization algorithm. The Harmony Search algorithm is an optimization technique which is often used to solve NP-complete problems, i.e. problems which deterministic approaches to solve are not efficient for. The results have been compared to the Particle Swarm Optimization algorithm and have shown that Harmony Search gives better area coverage.
Abstract:A new metaheuristic method applied to the global path planning for mobile robots in dynamic environments is presented. This algorithm, named the Quad Harmony Search method, consists of dividing the robot"s environment into free regions by applying the Quad-tree algorithm and utilizing this information to accelerate the next phase which implements the Harmony Search optimization method to provide the optimal route. The presented results have displayed that this method gives best results when compared to other metaheuristic techniques and is therefore applicable to the global path planning problem.
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