A Hybrid Strategy of Differential Evolution and Modified Particle Swarm Optimization for Numerical Solution of a Parallel Manipulator

Abstract

This paper presents a hybrid strategy combined with a differential evolution (DE) algorithm and a modified particle swarm optimization (PSO), denominated as DEMPSO, to solve the nonlinear model of the forward kinematics. The proposed DEMPSO takes the best advantage of the convergence rate of MPSO and the global optimization of DE. A comparison study between the DEMPSO and the other optimization algorithms such as the DE algorithm, PSO algorithm, and MPSO algorithm is performed to obtain the numerical solution of the forward kinematics of a 3-RPS parallel manipulator. The forward kinematic model of the 3-RPS parallel manipulator has been developed and it is essentially a nonlinear algebraic equation which is dependent on the structure of the mechanism. A constraint equation based on the assembly relationship is utilized to express the position and orientation of the manipulator. Five configurations with different positions and orientations are used as an example to illustrate the effectiveness of the proposed DEMPSO for solving the kinematic problem of parallel manipulators. And the comparison study results of DEMPSO and the other optimization algorithms also show that DEMPSO can provide a better performance regarding the convergence rate and global searching properties.

An Enriched Prediction Intervals Construction Method with Hybrid Intelligent Optimization

“…7with respect to the best output weights in the ELM model. To minimize the constrained optimization function and optimize the parameters of ELM, conventional optimization methods, such as Dynamic Programming (DP), Particle Swarm Optimization (PSO) algorithm, always suffer from the problem of being trapped into local optima [26][27][28][29][30]. Inspired by quantum mechanics, a new version of PSO named Quantum-behaved Particle Swarm Optimization (QPSO) [31] was proposed due to its guaranteed characteristic of global convergence.…”

Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm

“…The incorporation of the PSO phase generates a disturbance in the population, which aids in population diversification and the output of an optimum solution. The following is an illustration of the algorithm's procedure [19]:…”

“…Additionally, the associated velocities of all particles in the population are generated randomly in the D-dimension space. Therefore, the initial individuals and the initial velocity can be expressed as follows [19]:…”

“…Iteration Loop of DE: The individual mutation operation is denoted as time t. By randomly choosing three individuals from the previous population, the mutant individual (t + 1) can be generated as [19]:…”

Dual-arm cooperative manipulation based on differential evolution

“…[10][11][12][13][14] A hybrid algorithm between differential evolution (DE) and modified particle swarm optimization (MPSO) has been proposed to solve the inverse kinematics of serial manipulators 15 and parallel manipulators. 16 This method is called DEMPSO and it performed better than DE and MPSO versions. Ren et al 17 present a hybrid approach for solving the inverse kinematics of a serial manipulator based on DE and biogeography-based optimization (BBO).…”