Abstract
This paper introduces a hybrid metaheuristic algorithm that combines Particle Swarm Optimization (PSO) algorithm with Sine Cosine algorithm and Mathematical equations. The algorithm makes a contribution to optimization field by providing better strategy for finding the global minimal value, enhancing exploration and exploitation features, speeding up the converge rate over the tested benchmark optimization problems. The results show that combining SCA and ME with PSO in a new hybrid algorithm called PSE. However, the new algorithm overcome the drawbacks of PSO and it effectively solve high dimensional optimization problems. PSE algorithm is being applied in order to enhance robot path planning, robots can find a high efficiency specification objective when powered by hybrid algorithm. The objective of optimization is to reduce the path lengths to target. Autonomous path planning is necessary to prevent obstacles during the motion of robot. The result show that the algorithm proposed is able to give better performance in reaching targets.
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References
Al Sayyed, R.M., Fakhouri, H.N., Rodan, A., Pattinson, C.: Polar particle swarm algorithm for solving cloud data migration optimization problem. Mod. Appl. Sci. 11(8), 98 (2017)
Altay, E.V., Alatas, B.: Performance comparisons of socially inspired metaheuristic algorithms on unconstrained global optimization. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds.) Advances in Computer Communication and Computational Sciences, pp. 163–175. Springer, Singapore (2019)
Amodeo, L., Talbi, E.G., Yalaoui, F.: Recent Developments in Metaheuristics. Springer, Cham (2018)
Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft. Comput. 1, 20 (2018)
Krawiec, K., Simons, C., Swan, J., Woodward, J.: Metaheuristic design patterns: new perspectives for larger scale search architectures. In: Handbook of Research on Emergent Applications of Optimization Algorithms, pp. 1–36. IGI Global (2018)
Stützle, T., López Ibáñez, M.: Automated design of metaheuristic algorithms. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics, pp. 541–579. Springer, Cham (2019)
Hudaib, A.A., Fakhouri, H.N.: Supernova optimizer: a novel natural inspired meta heuristic. Mod. Appl. Sci. 12(1), 32 (2017)
Ong, P., Chin, D.D.V.S., Ho, C.S., Ng, C.H.: Metaheuristic approaches for extrusion manufacturing process: utilization of flower pollination algorithm and particle swarm optimization. In: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, pp. 43–56. IGI Global (2018)
Trivedi, I.N., et al.: A novel hybrid PSO–WOA algorithm for global numerical functions optimization. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds.) Advances in Computer and Computational Sciences, pp. 53–60. Springer, Singapore (2018)
Chegini, S.N., Bagheri, A., Najafi, F.: PSOSCALF: a new hybrid PSO based on Sine Cosine Algorithm and Levy flight for solving optimization problems. Appl. Soft Comput. 73, 697–726 (2018)
Mirjalili, S.M., et al.: Sine cosine algorithm: theory, literature review, and application in designing bend photonic crystal waveguides. In: Mirjalili, S., Song, D.J., Lewis, A. (eds.) Nature Inspired Optimizers, pp. 201–217. Springer, Cham (2019)
Kennedy, J.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2011)
Salcedo Sanz, S.: Modern meta heuristics based on nonlinear physics processes: a review of models and design procedures. Phys. Rep. 655(1), 70 (2016)
Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)
Glover, F.: Tabu search – part I. ORSA J. Comput. 1, 190–206 (1989)
van Laarhoven, P.J., Aarts, E.H.: Simulated annealing. In: van Laarhoven, P.J.M., Aarts, E.H.L. (eds.) Simulated Annealing: Theory and Applications, pp. 7–15. Springer, Dordrecht (1987)
Mladenović, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24(11), 1097–1100 (1997)
Krishnanand, K., Ghose, D.: Glowworm swarm optimization: a new method for optimising multi modal functions. Int. J. Comput. Intell. Stud. 1, 93–119 (2009)
Yang, X.S.: A new meta heuristic bat inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010)
Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Proceedings NATO Advanced Workshop on Robots and Biological Systems, Tuscany, 26–30 June 1989. https://doi.org/10.1007/978-3-642-58069-7_38
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Fakhouri, H.N., Hudaib, A., Sleit, A. (2020). Hybrid Particle Swarm Optimization with Science Cosine Algorithm and Mathematical Equations for Enhancing Robot Path Planning. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_23
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