Global–local population memetic algorithm for solving the forward kinematics of parallel manipulators

Abstract

Memetic algorithms (MA) are evolutionary computation methods that employ local search to selected individuals of the population. This work presents global-local population MA for solving the forward kinematics of parallel manipulators. A real-coded generation algorithm with features of diversity is used in the global population and an evolutionary algorithm with parent-centric crossover operator which has local search features is used in the local population. The forward kinematics of the 3RPR and 6-6 leg manipulators are examined to test the performance of the proposed method. The results show that the proposed method improves the performance of the real-coded genetic algorithm and can obtain highquality solutions similar to the previous methods for the 6-6 leg manipulator. The accuracy of the solutions and the optimisation time achieved by the methods in this work motivates for real-time implementation of the 3RPR parallel manipulator.

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

“…Analysis of kinematics can be divided into two approaches: analytical methods and numerical methods [17,18]. In numerical methods, the forward kinematic solution is found by solving a nonlinear global optimization problem to find the numerical solution [19].…”

Control distribution of partially decoupled multi-level manipulators with five DOFs

“…In addition to these two main trends, recent works can also be mentioned in which computational methods are developed to solve the kinematic problem of these manipulators. In 33 and, 34 a new algorithm is presented to solve the forward kinematic problem of parallel manipulators, with improved accuracy and optimized time when compared with previous methods.…”