Optimal Design of Multibody Systems by Using Genetic Algorithms

Abstract

The design step of multibody systems requires in some specific cases an optimization process, in order to determine the set of parameters which lead to optimal kinematic or dynamic performances. The aim of this paper is to propose an optimal design method adapted to general multibody systems and submitted to kinematic and/or dynamic time-dependent criteria. The optimization process is based on stochastic techniques referred to genetic algorithms, which are inspired from natural evolution and can often overtop classical optimization methods when applied to practical problems. Illustrative examples are given in the context of the optimization of the kinematics of a motorcar suspension and the lateral dynamics of an urban railway vehicle.

Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation

“…Application of the Synthesis Problem Metaheuristic Algorithms CHT [19] Four-bar mechanism Genetic Algorithm - [20] Four-bar mechanism Genetic Algorithm -Stephenson's six-bar mechanism Watt's six-bar mechanism [5] Four-bar mechanism Genetic Algorithm Penalty Function [21] Hand robot mechanism Pareto Optimum Evolutionary Feasibility rules Multiobjective Algorithm (POEMA) [22] Four-bar mechanism Differential Evolution Penality Function [23] Six-bar mechanism Differential Evolution Penality Function [24] Four-bar mechanism Differential Evolution Penality Function [25] Four-bar mechanism Genetic algorithm-fuzzy logic Penality Function [26] Four-bar and six-bar mechanisms MUMSA Penality Function [27] Four-bar mechanism Genetic Algorithm, Penality Function Differential Evolution, Particle Swarm Optimization [28] Four-bar mechanism Ant-gradient Penality Function [29] Four-bar mechanism GA-DE Penality Function [30] Six-bar mechanism Cuckoo Search Penality Function [31] Four-bar mechanism NSGA-II Feasibility rules [32] Four-bar mechanism Imperialist competitive algorithm, Penality Function Genetic Algorithm, Differential Evolution, Particle Swarm Optimization [33] Four-bar mechanism Modified Krill Herd Penality Function [34] Four-bar mechanism TLBO Penality Function Genetic Algorithm, Particle Swarm Optimization [35] Four-bar mechanism Hybrid Lagrange Interpolation DE Penality Function (HLIDE) [36] Four-bar and six-bar mechanisms Hybridization Differential Evolution Penality Function with Generalized Reduced Gradient Mechanisms for rehabilitation [18] Spherical parallel manipulator NSGA-II, MOPSO, MOEA/D Feasibility rules in prosthetic wrist [14] Six-bar mechanism in MUMSA Feasibility rules finger rehabilitation [15] Cam-linkage mechanism Genetic Algorithm Penalty Function. in gait rehabilitation [16] Four-bar mechanism Differential Evolution Feasibility rules in gait rehabilitation [17] Four-bar mechanism in Particle Swarm Optimization and TLBO gait rehabilitation and orthotic devices [3] Eight-bar mechanism in Differential Evolution Feasibility rules.…”

“…In this work, the number of violated constraints is considered the sum of infeasible constraint distance φ(x) and shown in (20).…”