"4"^^ . "*Enhancement of Suspension System Performance Performance of Heavy Vehicles through the Optimized Parameters Using Particle Swarm Technique (ICCV Paris)" . . . "[9BA9821685D0]" . "4"^^ . "RIV/68407700:21220/14:00227476" . "*Enhancement of Suspension System Performance Performance of Heavy Vehicles through the Optimized Parameters Using Particle Swarm Technique (ICCV Paris)"@en . "*Enhancement of Suspension System Performance Performance of Heavy Vehicles through the Optimized Parameters Using Particle Swarm Technique (ICCV Paris)" . "http://waset.org/Publications/XML?abstract=14423&t=bibtex" . . . "El Sawaf, Ahmed" . . . . "Toronto" . "7"^^ . . "World Academy of Science, Engineering and Technology" . "*Particle swarm optimization (PSO) algorithm has attracted significant consideration among a lot of modern heuristic optimization techniques. Nowadays, PSO is widely applied in various scientific and engineering fields. This paper concerns the investigation of design the passive suspension system parameters of heavy vehicles using the non-linear PSO algorithm, for the first time. A mathematical model and the equations of motion of a passive quarter vehicle suspension are derived and simulated using Matlab/Simulink software. The proposed PSO algorithm aims to minimize the dynamic tyre load generated by vehicle\u2013pavement interaction as the objective function with constraint the natural frequency of the unsprung mass. It is applied to solve the nonlinear optimization problem to find the tyre stiffness, suspension stiffness and the damping coefficient of the passive damper by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm. Suspension performance criteria are evaluated in the time and frequency domains in order to quantify the obtained parameters under bump and random road disturbance. Compared with the passive suspension system optimized using the Genetic Algorithm (GA), the proposed PSO algorithm improves the suspension performance effectively and gives a superior performance."@en . "Vampola, Tom\u00E1\u0161" . "*Enhancement of Suspension System Performance Performance of Heavy Vehicles through the Optimized Parameters Using Particle Swarm Technique (ICCV Paris)"@en . "*Particle swarm optimization (PSO) algorithm has attracted significant consideration among a lot of modern heuristic optimization techniques. Nowadays, PSO is widely applied in various scientific and engineering fields. This paper concerns the investigation of design the passive suspension system parameters of heavy vehicles using the non-linear PSO algorithm, for the first time. A mathematical model and the equations of motion of a passive quarter vehicle suspension are derived and simulated using Matlab/Simulink software. The proposed PSO algorithm aims to minimize the dynamic tyre load generated by vehicle\u2013pavement interaction as the objective function with constraint the natural frequency of the unsprung mass. It is applied to solve the nonlinear optimization problem to find the tyre stiffness, suspension stiffness and the damping coefficient of the passive damper by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm. Suspension performance criteria are evaluated in the time and frequency domains in order to quantify the obtained parameters under bump and random road disturbance. Compared with the passive suspension system optimized using the Genetic Algorithm (GA), the proposed PSO algorithm improves the suspension performance effectively and gives a superior performance." . . "Suspension system; heavy vehicles, parameters design; parameters optimization; particle swarm technique"@en . . "RIV/68407700:21220/14:00227476!RIV15-MSM-21220___" . . "21220" . "Metered, Hassan Ahmed Mohamed" . . "El Sawaf, Ahmed" . "Brussels" . "\u0160ika, Zbyn\u011Bk" . "2014-10-05+02:00"^^ . "14683" . "1307-6892" . . . "P(LO1311)" . . . . "Metered, Hassan Ahmed Mohamed" . . "ICAMME 2014 - Transactions on Mechanical and Mechatronics Engineering Vol:2, No:10, 2014" . .