Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem

This study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.

[1]  U. Walter,et al.  Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO) , 2015 .

[2]  Ali Kaveh,et al.  Chaos-based firefly algorithms for optimization of cyclically large-size braced steel domes with multiple frequency constraints , 2019, Computers & Structures.

[3]  Yu Xue,et al.  A hybrid multi-objective firefly algorithm for big data optimization , 2017, Appl. Soft Comput..

[4]  Masoud Monjezi,et al.  Prediction of seismic slope stability through combination of particle swarm optimization and neural network , 2015, Engineering with Computers.

[5]  R. Venkata Rao,et al.  A new optimization algorithm for solving complex constrained design optimization problems , 2017 .

[6]  Zhenzhen Zhang,et al.  A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints , 2018, Eur. J. Oper. Res..

[7]  Chunguo Wu,et al.  Particle swarm optimization based on dimensional learning strategy , 2019, Swarm Evol. Comput..

[8]  Zhihua Cui,et al.  Bat algorithm with triangle-flipping strategy for numerical optimization , 2018, Int. J. Mach. Learn. Cybern..

[9]  B. Walczak,et al.  Particle swarm optimization (PSO). A tutorial , 2015 .

[10]  Yudong Zhang,et al.  A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications , 2015 .

[11]  G. R. Gogate Inverse kinematic and dynamic analysis of planar path generating adjustable mechanism , 2016 .

[12]  Ben J Hicks,et al.  Improving the design of high speed mechanisms through multi-level kinematic synthesis, dynamic optimization and velocity profiling , 2017 .

[13]  Bahriye Akay,et al.  Comparisons of metaheuristic algorithms and fitness functions on software test data generation , 2016, Appl. Soft Comput..

[14]  Najeh Ben Guedria,et al.  Improved accelerated PSO algorithm for mechanical engineering optimization problems , 2016, Appl. Soft Comput..

[15]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Timur Inan,et al.  Particle swarm optimization-based collision avoidance , 2019 .

[17]  Farshad Kowsary,et al.  Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .

[18]  Dervis Karaboga,et al.  A quick semantic artificial bee colony programming (qsABCP) for symbolic regression , 2019, Inf. Sci..

[19]  Theoklis Nikolaidis,et al.  Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; A review, research challenges, and exploring the future , 2019, Progress in Aerospace Sciences.

[20]  Liying Wang,et al.  Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications , 2020, Eng. Appl. Artif. Intell..

[21]  B. B. Choudhury,et al.  Inverse Kinematics Solution of a 6-DOF Industrial Robot , 2019 .

[22]  Riccardo Patriarca,et al.  Modeling and Quantification of Resilience in Complex Engineering Systems , 2019, Complex..

[23]  A ArockiaSelvakumar,et al.  Kinematic and Dynamic Analysis of 3PUU Parallel Manipulator for Medical Applications , 2018 .

[24]  Serkan Dereli,et al.  Calculation of the inverse kinematics solution of the 7-DOF redundant robot manipulator by the firefly algorithm and statistical analysis of the results in terms of speed and accuracy , 2020, Inverse Problems in Science and Engineering.

[25]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[26]  Qian Zhang,et al.  Artificial intelligence in recommender systems , 2020, Complex & Intelligent Systems.

[27]  Stan Matwin,et al.  A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data , 2013, Artificial Intelligence Review.

[28]  Yi Liu,et al.  Production scheduling optimization method based on hybrid particle swarm optimization algorithm , 2018, J. Intell. Fuzzy Syst..

[29]  Mangey Ram,et al.  System Reliability Optimization Using Gray Wolf Optimizer Algorithm , 2017, Qual. Reliab. Eng. Int..

[30]  Peng Yang,et al.  Variable neighborhood search heuristic for storage location assignment and storage/retrieval scheduling under shared storage in multi-shuttle automated storage/retrieval systems , 2015 .

[31]  Yasuteru Shigeta,et al.  TaBoo SeArch Algorithm with a Modified Inverse Histogram for Reproducing Biologically Relevant Rare Events of Proteins. , 2016, Journal of chemical theory and computation.

[32]  Aise Zülal Sevkli,et al.  StPSO: Strengthened particle swarm optimization , 2010 .

[33]  Emad Issa Abdul Kareem,et al.  Traffic Light Controller Module Based on Particle Swarm Optimization (PSO) , 2018 .

[34]  Ying Cui,et al.  Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles , 2019, Sensors.

[35]  Chun Man Chan,et al.  Blade Shape Optimization of the Savonius Wind Turbine Using a Genetic Algorithm , 2018 .

[36]  Borja García de Soto,et al.  BIM-based Applications of Metaheuristic Algorithms to Support the Decision-making Process: Uses in the Planning of Construction Site Layout , 2017 .

[37]  Mahdi Hasanipanah,et al.  Prediction of blast-produced ground vibration using particle swarm optimization , 2017, Engineering with Computers.

[38]  Zhaoxing Li,et al.  Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization , 2018, Sustainability.

[39]  Seyda Topaloglu,et al.  An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries , 2015, Comput. Ind. Eng..

[40]  Vimal Kumar Pathak,et al.  Evaluating Geometric Characteristics of Planar Surfaces using Improved Particle Swarm Optimization , 2017 .

[41]  Dario Pacciarelli,et al.  Ant colony optimization for the real-time train routing selection problem , 2016 .