Grey Particle Swarm Optimization Research Articles

Improved Grey Particle Swarm Optimization and New Luus‐Jaakola Hybrid Algorithm Optimized IMC‐PID Controller for Diverse Wing Vibration Systems

The PID control plays important role in wing vibration control systems. However, how to efficiently optimize the PID parameters for different kinds of wing vibration systems is still an open issue for control designers. The problem of PID control optimization is first converted into internal mode control based PID (IMC‐PID) parameters optimization problem for complex wing vibration systems. To solve this problem, a novel optimization technique, called GNPSO is proposed based on the hybridization of improved grey particle swarm optimization (GPSO) and new Luus‐Jaakola algorithm (NLJ). The original GPSO is modified by using small population size/iteration number, employing new grey analysis rule and designing new updating formula of acceleration coefficients. The hybrid GNPSO benefits improved global exploration of GPSO and strong local search of new Luus‐Jaakola (NLJ), so as to avoid arbitrary and inefficient search of global optimum and prevent the trap in local optimum. Diverse wing vibration systems, including linear system, nonlinear system and multiple‐input‐multiple‐output system are considered to verify the effectiveness of proposed method. Simulation results show that GNPSO optimized method obtains improved vibration control performance, stronger robustness and wide applicability on all system cases, compared to existing evolutionary algorithm based tuning methods. Enhanced optimization convergence and computation efficiency obtained by GNPSO tuning technique are also verified by statistical analysis.

Multiobjective Optimal Design of Vehicle Suspension Parameters Based on Reliable Gray Particle Swarm Optimization

AbstractThis study presents a multiobjective optimal design of automobile suspension systems to improve vehicle-ride comfort and reduce tire-induced dynamic excitations on road surface simultaneously. In the optimal model, spring stiffness and the damper coefficient are considered design variables, whereas the maximum deflection of the suspension system is regarded as a constraint. Meanwhile, the root-mean-square values of the vertical acceleration of the vehicle body and the dynamic loadings of the front and rear tires are treated as objective functions. Multiobjective optimization is implemented using the gray particle swarm algorithm. Globally optimal solutions are obtained by introducing the variance of relevant sequence numbers into gray relevant theory. A half-car model is used to illustrate the proposed optimal model and solution method. Results show that the minimum acceleration of the vehicle body and the minimum dynamic loads exerted by tires on road surfaces can be achieved through the proposed...

Particle swarm optimization with grey evolutionary analysis

Based on grey relational analysis, this study attempts to propose a grey evolutionary analysis (GEA) to analyze the population distribution of particle swarm optimization (PSO) during the evolutionary process. Then two GEA-based parameter automation approaches are developed. One is for the inertia weight and the other is for the acceleration coefficients. With the help of the GEA technique, the proposed parameter automation approaches would enable the inertia weight and acceleration coefficients to adapt to the evolutionary state. Such parameter automation behaviour also makes an attempt on the GEA-based PSO to perform a global search over the search space with faster convergence speed. In addition, the proposed PSO is applied to solve the optimization problems of twelve unimodal and multimodal benchmark functions for illustration. Simulation results show that the proposed GEA-based PSO could outperform the adaptive PSO, the grey PSO, and two well-known PSO variants on most of the test functions.

Multi-Objective Optimization Design for Gear Reducer Based on the Grey Particle Swarm Algorithm

Combining the thought of correlation degree analysis in the theory of grey, use of particle swarm algorithm, seeking it’s individual extreme value and global extreme value, and puts forward to the goal of mathematical model about more gray particle swarm optimization algorithm is presented, the algorithm is applied to speed reducer hoisting mechanism in the optimization of parameters. The optimization results show that the optimal parameters, than the original design of parameters for satisfactory results show the particle swarm optimization algorithm is used for gray hoisting mechanism optimized parameter design of gear reducer is effective and feasible.

Grey particle swarm optimization

With the help of grey relational analysis, this study attempts to propose two grey-based parameter automation strategies for particle swarm optimization (PSO). One is for the inertia weight and the other is for the acceleration coefficients. By the proposed approaches, each particle has its own inertia weight and acceleration coefficients whose values are dependent upon the corresponding grey relational grade. Since the relational grade of a particle is varying over the iterations, those parameters are also time-varying. Even if in the same iteration, those parameters may differ for different particles. In addition, owing to grey relational analysis involving the information of population distribution, such parameter automation strategies make an attempt on the grey PSO to perform a global search over the search space with faster convergence speed. The proposed grey PSO is applied to solve the optimization problems of 12 unimodal and multimodal benchmark functions for illustration. Simulation results are compared with the adaptive PSO (APSO) and two well-known PSO variants, PSO with linearly varying inertia weight (PSO-LVIW) and PSO with time-varying acceleration coefficients (HPSO-TVAC), to demonstrate the search performance of the grey PSO.

Multi-objective Optimization Design on Gear Train of Planetary Reducer in Shield Tunneling Machine Based on Reliably Grey Particle Swarm Optimization

The sigma is introduced into grey relational degree,the notion of reliably grey relational degree is creatively put forward to be used as mutation strategy to control the basic particle swarm optimization(PSO),which may avoid the problem that the algorithm prematurely falls into local optimum.For those variables called for integers,their initial position and speed of each flight are taken as integers,to ensure that the obtained solutions are integral.Meanwhile,to understand easily and operate conveniently,the mechanism and processes of the algorithm are illustrated,thus,a practical and convenient method is provided for multi-objective mixed integer programming problems subject to complex non-linear conditions.The mathematical models are constructed for planetary gear reducer in shield tunneling machine(STM) with 4 objective functions,that is to make its volume smallest,its efficiency highest and the reliability of its contacting and bending strength highest,which are subject to the conditions such as teeth,modification coefficient,interference,strength,equal strength and equal life on the basis of the 4 assumptions,which are solved by means of Matlab programming language compiled by using the reliably grey PSO.The research results show that generalization ability not only resolves the strength problem which is a little weak in the original 3rd grade of the planetary gear reducer,but also decreases the volume by 11.55% and increases the efficiency by 0.56% and the transmission ratio by 3.67%,and the strength and life in every grade are basically equal under the condition of ensuring high reliability.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.