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

Abstract

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.

An Equilibrium Optimizer Slime Mould Algorithm For Inverse Kinematics Of The 7-DOF Robotic Manipulator

“…According to the results, QPSO has the best problem-solving performance; Dereli and Köker [9] applied QPSO to solve the IK of 7-DOF serial manipulator and compared it with FA, PSO, and ABC. The results show that QPSO has higher solving accuracy and shorter calculation time than the contrast algorithm; Liu et al [60] proposed a parallel learning PSO (PLPSO) to solve the IK problem and verified the practicability and feasibility of the algorithm on UR5 manipulator; Dereli and Köker [61] proposed a RDV-PSO that combines golf ball movements and PSO, and applied it to the IK solution of 7-DOF manipulator; Momani et al [62] applied the traditional GA and the continuous GA to the IK problem respectively, and the results showed that the continuous GA was superior to the traditional GA in all aspects; López-Franco et al [63] applied DE to the IK of the manipulator. Simulation and experimental results show the applicability of this method; Rokbani et al [64] applied FA to the IK problem and tested it on a three-link articulated planar system, and conducted a statistical analysis on the convergence and solution quality of 100 tests; Dereli and Köker [12] applied FA to the IK problem of a 7-DOF redundant manipulator and compared it with PSO and ABC; Çavdar and Milani [65] proposed a method for solving IK of a robot manipulator based on improved ABC, and the results illustrate that the proposed algorithm outperforms PSO and HS in positioning accuracy and solving time; El-Sherbiny et al [66] proposed K-ABC, which used different parameters in the process of updating food sources, and then used K-ABC to calculate the IK of a 5-DOF manipulator.…”

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

“…The results show that QPSO has higher solving accuracy and shorter calculation time than the contrast algorithm; Liu et al . 66 proposed a parallel learning PSO (PLPSO) to solve the IK problem and verified the practicability and feasibility of the algorithm on UR5 manipulator; Dereli and Köker 67 proposed a RDV-PSO that combines golf ball movements and PSO, and applied it to the IK solution of 7-DOF manipulator; Momani et al . 68 applied the traditional GA and the continuous GA to the IK problem respectively, and the results showed that the continuous GA was superior to the traditional GA in all aspects; López-Franco et al .…”

Multiple Ant Colony Algorithm Combining Community Relationship Network

“…Metaheuristic algorithms, also known as intelligent optimization algorithms, differ from optimization algorithms in that they can construct a feature model based on a specific problem and find a feasible solution under specified constraints. Many scholars have studied meta-heuristic algorithms in order to solve different complex problems, such as genetic algorithm [4], particle swarm optimization [5], grey wolf optimization algorithm [6], whale optimization algorithm [7]. As a typical swarm intelligence algorithm, the ant colony algorithm has excellent ability to find the optimal solution in solving NP-hard problems, which has profound research value and significance.…”