Modified Cuckoo Search Based Neural Networks for Forest Types Classification.
01 Jan 2017-Vol. 296, pp 490-498
About: The article was published on 2017-01-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Cuckoo search & Artificial neural network.
TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.
Abstract: The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.
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