The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to
determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum
path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful
solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research
work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on
the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning
on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.
Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.
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