A modified particle swarm optimizer and its application to spatial truss topological optimization

p. 1044-1057Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence which is inspired by concepts from 'Social Psychology' and 'Artificial Life'. Essentially, PSO proposes that the co-operation of individuals promotes the evolution of the swarm. In terms of optimization, the hope would be to enhance the swarm's ability to search on a global scale so as to determine the global optimum in a fitness landscape. It has been empirically shown to perform well with regard to many different kinds of optimization problems. PSO is particularly a preferable candidate to solve highly nonlinear, non-convex and even discontinuous problems. In this paper, one enhanced version of PSO: Modified Lbest based PSO (LPSO) is proposed and applied to one of the most challenging fields of optimization -- truss topological optimization. Through a benchmark test and a spatial structural example, LPSO exhibited competitive performance due to improved global searching ability.Yang, B.; Bletzinger, K. (2009). A modified particle swarm optimizer and its application to spatial truss topological optimization. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/676

Inverse Geometry Design of Radiative Enclosures Using Particle Swarm Optimization Algorithms

Three different Particle Swarm Optimization (PSO) algorithms—standard PSO, stochastic PSO (SPSO) and differential evolution PSO (DEPSO)—are applied to solve the inverse geometry design problems of radiative enclosures. The design purpose is to satisfy a uniform distribution of radiative heat flux on the designed surface. The design surface is discretized into a series of control points, the PSO algorithms are used to optimize the locations of these points and the Akima cubic interpolation is utilized to approximate the changing boundary shape. The retrieval results show that PSO algorithms can be successfully applied to solve inverse geometry design problems and SPSO achieves the best performance on computational time. The influences of the number of control points and the radiative properties of the media on the retrieval geometry design results are also investigated

A Novel Assembly Line Scheduling Algorithm Based on CE-PSO

With the widespread application of assembly line in enterprises, assembly line scheduling is an important problem in the production since it directly affects the productivity of the whole manufacturing system. The mathematical model of assembly line scheduling problem is put forward and key data are confirmed. A double objective optimization model based on equipment utilization and delivery time loss is built, and optimization solution strategy is described. Based on the idea of solution strategy, assembly line scheduling algorithm based on CE-PSO is proposed to overcome the shortcomings of the standard PSO. Through the simulation experiments of two examples, the validity of the assembly line scheduling algorithm based on CE-PSO is proved

Parametric Study of Coal Liberation Behavior Using Silica Grinding Media

This study evaluates the coal liberation behavior using silica as the grinding media by assessing the effects of four operating factors including nominal feed size, media size, shaft speed and grinding time, each of three levels on two response variables, the product P80 and the specific energy. The coal material used in this study was mixed-phase particles commonly referred to as middlings, sampled from dense medium circuit at the Leatherwood preparation plant in Kentucky. One-third fractional factorial design of resolution III was implemented. Since silica was obtained in three standard size ranges as per manufacturer’s design, media size was qualitative while other factors were quantitative. The experiment was custom designed, and the results were analyzed with JMP statistical software. Both ash analysis of the grind products and the shape analysis of the media before and after the grinding tests indicate no media degradation occurred during the grinding process. Statistical analyses were initially performed to determine the operating parameters that significantly influence the product P80 and the specific energy. For the product P80, feed size has a p-Value of 0.001 at a five-percent significance level. In addition, the normal probability plot of effect estimates also shows feed size deviates from the straight line. Hence, only feed size amongst the four operating factors has a significant effect on the product P80. However, for specific energy, grinding time and shaft speed have p-Values of 0.03 and 0.05, respectively at a five-percent significance level. This is also corroborated on the normal probability plot of effect estimates where only grinding time and shaft speed deviate from the straight line. Therefore, only grinding time and shaft speed significantly influence specific energy. Based on the mathematical models that were further developed, it can be deduced that the product P80 decreases with decreasing feed size and vice versa. On the other hand, the specific energy decreases with decreasing grinding time and shaft speed and vice versa. Irrespective of other factors investigated in this study, the lowest and highest product P80 (4.5 microns and 137.5 microns, respectively) were measured when the nominal feed size was at its low level (25 microns) and high level (250 microns), respectively. In the same vein, the lowest and highest specific energy (16 kWh/ton and 416 kWh/ton, respectively) were obtained when grinding time and shaft speed were at their low levels (16 minutes and 200 rpm) and high levels (64 minutes and 400 rpm), separately. Finally, the batch grinding process was numerically simulated with the population balance model using the experimental data. Particle swarm optimization, a stochastic algorithm in MATLAB was used to iteratively fit the model to the experimental data with a mean squared error of 0.01. The selection and breakage function parameters of Leatherwood coal were determined as 0.05, 4.98, -2.03 and 1.34, 0.06, 10.15, respectively

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field