General Particle Swarm Optimization Algorithm for Integration of Process Planning and Scheduling

2010 ◽  
Vol 118-120 ◽  
pp. 409-413
Author(s):  
Shao Tan Xu ◽  
Xin Yu Li ◽  
Liang Gao ◽  
Yi Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Process Planning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Varying ◽  
Step Size ◽  
Swarm Optimization ◽  
Planning And Scheduling ◽  
System A

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu search (TS) is used for particles’ local search. And time varying crossover probability and time varying maximum step size of tabu search are introduced. Experimental results show that IPPS can be solved by GPSO effectively. The feasibility of the proposed GPSO model and the significance of the research on IPPS are also demonstrated.

Research and Design of Adaptive Noise Cancellation Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 479-481 ◽  
pp. 1942-1945
Author(s):  
Jie Zhang ◽  
Shi Qi Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Noise Cancellation ◽  
Biological Species ◽  
Lms Algorithm ◽  
Adaptive Noise Cancellation ◽  
Step Size ◽  
Swarm Optimization ◽  
Adaptive Noise

Particle swarm optimization (PSO) is a kind of evolutionary computation technology which simulates the behavior of biological species. The essence of adaptive noise cancellation (ANC) is adjust the weight value of filter based on the input signals, the LMS algorithm is commonly used in this system, However, the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size μ, and the optimum value of μ cannot be determined easily, In this paper, Particle Swarm Optimization with linear decreasing inertia weight is proposed to solve the filter problem instead of LMS, taking the FIR filter of ANC as example, the simulation shows that ANC based on the PSO algorithm is better than classic ANC based on the LMS algorithm, and it gives the satisfactory results.

Particle Swarm Optimization Using Various Inertia Factor Variants

2011 ◽  
Vol 460-461 ◽  
pp. 54-59
Author(s):  
Jun Tang
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of manufacturing systems. Three different inertia factor, a constant inertia factor (CIF), time-varying inertia factor (TVIF), and global-local best inertia factor (GLbestIF), are considered with the particle swarm optimization(PSO) algorithm to analyze the impact of inertia factor on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia factor separately to compute the optimal control of the manufacturing system, and it is observed that the PSO with the proposed inertia factor yields better resultin terms of both optimal solution and faster convergence. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

scholarly journals A Crazy Particle Swarm Optimization with Time Varying Acceleration Coefficients for Economic Load Dispatch

2019 ◽  
Vol 9 (2) ◽  
pp. 1205-1213
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Economic Load Dispatch ◽  
Time Varying ◽  
Swarm Optimization ◽  
Real Coded Genetic Algorithm ◽  
Conventional Optimization

In power generating plants, the expenses on combustible fuel is extremely costly and the concept of ELD (Economic Load Dispatch) make possible to save the considerable portion of profits. Practically generators have economic dispatch problems in terms of non-convexity. These kinds of problem cannot be resolved by conventional optimization techniques because the complication escalates due to manifold constrained that require to be fulfilled in all operating conditions. Recently a Particle Swarm Optimization (PSO) algorithm stimulated by collective conduct of swarm can be applied effectively to translate the ELD problems. The classical PSO bears the difficulty of early convergence mainly when the space of search is asymmetrical. To overcome the trouble “Crazy PSO with TVAC (Time Varying Acceleration Coefficients)” is launched which improve the search ability of the PSO by rebooting the vector of velocity whenever diffusion or saturation locate inside and to employ a scheme of parameter automation to maintain correct equilibrium between global hunt and local hunt and also circumvent the congestion. This arrangement is developed crazy PSO with TVAC and also demonstrated on two different model experimental structures of three generation units and six generation units. The result acquired from proposed method is evaluate with classical PSO and Real coded genetic algorithm (RGA) and it is found to be superior. This method is mathematically simple, gives fast convergence and robustness to resolve the rigid optimization inconvenience.

ON THE ANALYSIS OF THE PERFORMANCES OF PARTICLE SWARM OPTIMIZATION ALGORITHM WITH GLOBALLY AND LOCALLY TUNED INERTIA WEIGHT VARIANTS

2006 ◽  
Vol 03 (01) ◽  
pp. 97-114 ◽  
Author(s):  
M. SENTHIL ARUMUGAM ◽  
MACHAVARAM VENKATA CHALAPATHY RAO
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Hybrid System ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Time Varying ◽  
Swarm Optimization ◽  
Inertia Weight

The investigation of the performance of Particle Swarm Optimization (PSO) algorithm with the new variants to inertia weight in computing the optimal control of a single stage hybrid system is presented in this paper. Three new variants for inertia weight are defined and their applicability with the PSO algorithm is thoroughly explained. The results obtained through the new proposed methods are compared with the existing PSO algorithm, which has a time varying inertia weight from a higher value to a lower value. The proposed methods provide both faster convergence and optimal solution with better accuracy.

Sign in / Sign up

Export Citation Format

Share Document