Generalized intelligent Water Drops algorithm by fuzzy local search and intersection operators on partitioning graph for path planning problem

Abstract

Abstract

In this paper, a generalized intelligent water drops algorithm (IWD) for solving robot path planning problem is proposed. The authors want to reduce the time of reaching the optimal solution as much as possible. To do this, some new heuristic operators and a multi section graph model of environment is introduced. The authors divide graph to equal sections and compare behaviour of the solutions (paths) in each section with behaviour of them in other sections. This comparison uses a fuzzy inference system. Base on this comparison, a fuzzy number is assigned to each part of solutions. This fuzzy number determines the worth of a solution in a section. Less worth solutions need more improvement. New heuristic operators are used in the sections that need more improvement. The runtime of algorithms are increased by using a memory for keep proper solutions and a global smooth operator that smooth the solutions. The authors used proposed memory to apply heuristic operations on proper solutions more than other one. This method helps to obtain more improvement in lower runtime. The authors introduce two intersection operators for robot path planning problem that apply to solutions in memory. Experimental results show that the proposed algorithms find the optimal solutions in fewer runtime rather than other works.