Abstract
Path planning represents an important optimization problem that need to be solved in various applications. It is a hard optimization problem thus deterministic algorithms are not usable but it can be tackled by stochastic population based metaheuristics such as swarm intelligence algorithms. In this paper we adopted and adjusted harmony search algorithm for the path planning problem in environment with static obstacles and danger zones. Objective function includes path length and safety degree. The proposed method was tested on standard benchmark examples from literature. Simulation results show that our proposed model produces better and more consistent results in spite of its simplicity.
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References
Alam, M.S., Rafique, M.U., Khan, M.U.: Mobile robot path planning in static environments using particle swarm optimization. Int. J. Comput. Sci. Electron. Eng. (IJCSEE) 3, 253–257 (2015)
Alam, M.S., Rafique, M.U.: Mobile robot path planning in environments cluttered with non-convex obstacles using particle swarm optimization. In: International Conference on Control, Automation and Robotics (ICCAR), pp. 32–36. IEEE (2015)
Chen, J., Cheng, S., Chen, Y., Xie, Y., Shi, Y.: Enhanced brain storm optimization algorithm for wireless sensor networks deployment. In: International Conference in Swarm Intelligence, pp. 373–381. Springer (2015)
Contreras-Cruz, M.A., Ayala-Ramirez, V., Hernandez-Belmonte, U.H.: Mobile robot path planning using artificial bee colony and evolutionary programming. Appl. Soft Comput. 30, 319–328 (2015)
Delmerico, J., Mueggler, E., Nitsch, J., Scaramuzza, D.: Active autonomous aerial exploration for ground robot path planning. IEEE Robot. Autom. Lett. 2(2), 664–671 (2017)
Dolicanin, E., Fetahovic, I., Tuba, E., Capor-Hrosik, R., Tuba, M.: Unmanned combat aerial vehicle path planning by brain storm optimization algorithm. Stud. Inf. Control 27(1), 15–24 (2018)
Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. Biosystems 43(2), 73–81 (1997)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43. IEEE (1995)
Geem, Z., Kim, J., Loganathan, G.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)
Li, B., Gong, L.g., Yang, W.l.: An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning. Sci. World J. 2014, 1–10 (2014). Article ID 232704
Liu, J., Yang, J., Liu, H., Tian, X., Gao, M.: An improved ant colony algorithm for robot path planning. Soft Comput. 21(19), 5829–5839 (2017)
Liu, Y., Zhang, X., Guan, X., Delahaye, D.: Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization. Aerosp. Sci. Technol. 58, 92–102 (2016)
Tuba, E., Tuba, M., Beko, M.: Node localization in ad hoc wireless sensor networks using fireworks algorithm. In: 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 223–229. IEEE (2016)
Tuba, E., Tuba, M., Beko, M.: Support vector machine parameters optimization by enhanced fireworks algorithm. In: International Conference in Swarm Intelligence, LNCS, vol. 9712, pp. 526–534. Springer (2016)
Tuba, E., Tuba, M., Dolicanin, E.: Adjusted fireworks algorithm applied to retinal image registration. Stud. Inf. Control 26(1), 33–42 (2017)
Tuba, E., Tuba, M., Simian, D.: Handwritten digit recognition by support vector machine optimized by bat algorithm. In: Computer Science Research Note: International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 369–376 (2016)
Tuba, E., Tuba, M., Simian, D., Jovanovic, R.: JPEG quantization table optimization by guided fireworks algorithm. In: IWCIA: Combinatorial Image Analysis, vol. 10256, pp. 294–307. Springer (2017)
Tuba, M., Bacanin, N.: JPEG quantization tables selection by the firefly algorithm. In: International Conference on Multimedia Computing and Systems (ICMCS), pp. 153–158. IEEE (2014)
Xianxiang, W., Yan, M., Juan, W.: An improved path planning approach based on particle swarm optimization. In: 11th International Conference on Hybrid Intelligent Systems (HIS), pp. 157–161. IEEE (2011)
Yang, X.S.: Harmony Search as a Metaheuristic Algorithm, pp. 1–14. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00185-7_1
Acknowledgment
This research is supported by Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
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Tuba, E., Strumberger, I., Bacanin, N., Tuba, M. (2020). Optimal Path Planning in Environments with Static Obstacles by Harmony Search Algorithm. In: Kim, J., Geem, Z., Jung, D., Yoo, D., Yadav, A. (eds) Advances in Harmony Search, Soft Computing and Applications. ICHSA 2019. Advances in Intelligent Systems and Computing, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-31967-0_21
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DOI: https://doi.org/10.1007/978-3-030-31967-0_21
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