Optimizing Cam Profiles Using the Particle Swarm Technique

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Ramiro H. Bravo ◽  
Forrest W. Flocker
Keyword(s):  
Cycle Time ◽  
Constant Velocity ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimization Procedure ◽  
Mathematical Function ◽  
Independent Variables ◽  
Negative Acceleration ◽  
Cam Follower

Cam-follower systems are ideally suited for many machine applications that require a specific and an accurate output motion. The required follower motion is achieved by carefully designing the shape or profile of the cam. Modern profiles are typically synthesized by piecing together a set of trigonometric and/or polynomial functions that satisfy the constraints. For most problems, there are many profile solutions that satisfy the constraints. In this paper, a relatively new optimization technique known as particle swarm optimization (PSO) is applied to the optimization of two different cam problems. The first example is a single-dwell cam in which the magnitude of the negative acceleration is minimized. The second example is a cam with a constant velocity segment in which the cycle time is optimized. The intent is to show the method in two different settings so that the reader can extend it to the optimization of any cam-follower problem. To illustrate the method, first the PSO method is applied to a mathematical function with two independent variables. Then, the method is used to find the cam profile that provides the minimum acceleration in a single-dwell cam using three independent variables. Finally, it is applied to obtain the minimum cycle time of a cam with a constant velocity segment using cubic interpolations and seven or more independent variables. The PSO method was very successful in all the optimization problems discussed in this paper. In the first cam problem, it significantly lowered the level of negative acceleration, while maintaining the positive acceleration at a constrained upper limit. The optimization procedure for the second cam problem found a very elegant five-segment solution. This solution results no matter how many initial segments or independent variables are chosen so long as there are at least five segments. Presented in this paper is the particle swarm optimizing technique that is applicable to many aspects of cam design. Two diverse examples were presented that illustrate how the PSO method can be used effectively in the optimization of cam-follower problems. In both illustrative examples, the PSO method proved to be robust, easy to implement, and suitable for minimizing a wide variety objective functions applicable to the design of cam-follower systems.

A Closed-Form Solution for Minimizing the Cycle Time in Motion Programs With Constant Velocity Segments

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Forrest W. Flocker ◽  
Ramiro H. Bravo
Keyword(s):  
Closed Form ◽  
Cycle Time ◽  
Constant Velocity ◽  
Closed Form Solution ◽  
Form Solution ◽  
Industrial Robots ◽  
General Applicability ◽  
Negative Acceleration ◽  
Cam Follower ◽  
Time Subject

An important need for some dynamic systems is to design a periodic motion program that has a constant velocity segment for a specified time. A few examples of such systems are cam-follower systems used in continuous motion manufacturing, linear actuators, space-based scanners, and industrial robots. In this paper, the closed-form solution is given for a motion program that minimizes the cycle time subject to user-specified limits on positive and negative acceleration and jerk. The main benefit of minimizing cycle time is to maximize the throughput. Two motion programs that address the problem are presented and critically examined. For general applicability, the solution is presented in dimensionless form and an example is given to show its implementation to a typical problem. Conclusions regarding the profiles are drawn and given.

scholarly journals Modified signomial geometric programming (MSGP) and its applications

2019 ◽  
Vol 10 (2) ◽  
pp. 440
Author(s):  
Wasim Akram Mandal ◽  
Sahidul Islam
Keyword(s):  
Nonlinear Optimization ◽  
Real World ◽  
Geometric Programming ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Mathematical Function ◽  
Mathematical Result ◽  
Independent Variables ◽  
Integral Degree ◽  
Signomial Geometric Programming

A "signomial" is a mathematical function, contains one or more independent variables. Richard J. Duffin and Elmor L. Peterson introduced the term "signomial". Signomial geometric programming (SGP) optimization technique often provides a much better mathematical result of real-world nonlinear optimization problems. In this research paper, we have proposed unconstrained and constrained signomial geometric programming (SGP) problem with positive or negative integral degree of difficulty. Here a modified form of signomial geometric programming (MSGP) has been developed and some theorems have been derived. Finally, these are illustrated by proper examples and applications.

On Global Convergence in Design Optimization Using the Particle Swarm Optimization Technique

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Forrest W. Flocker ◽  
Ramiro H. Bravo
Keyword(s):  
Particle Swarm Optimization ◽  
Global Minimum ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Objective Functions ◽  
Convergence Criteria ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique

The particle swarm optimization (PSO) method is becoming a popular optimizer within the mechanical design community because of its simplicity and ability to handle a wide variety of objective functions that characterize a proposed design. Typical examples arising in mechanical design are nonlinear objective functions with many constraints, which typically arise from the various design specifications. The method is particularly attractive to mechanical design because it can handle discontinuous functions that occur when the designer must choose from a discrete set of standard sizes. However, as in other optimizers, the method is susceptible to converging to a local rather than global minimum. In this paper, convergence criteria for the PSO method are investigated and an algorithm is proposed that gives the user a high degree of confidence in finding the global minimum. The proposed algorithm is tested against five benchmark optimization problems, and the results are used to develop specific guidelines for implementation.

A Hybrid Particle Swarm Optimization Algorithm for Dynamic Environments

2014 ◽  
Vol 926-930 ◽  
pp. 3338-3341
Author(s):  
Hong Mei Ni ◽  
Zhian Yi ◽  
Jin Yue Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Hybrid Particle Swarm Optimization ◽  
Moving Peaks Benchmark ◽  
Chaos Algorithm

Chaos is a non-linear phenomenon that widely exists in the nature. Due to the ease of implementation and its special ability to avoid being trapped in local optima, chaos has been a novel optimization technique and chaos-based searching algorithms have aroused intense interests. Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in static environments. When the particle swarm optimization (PSO) algorithm is used in dynamic multi-objective problems, there exist some problems, such as easily falling into prematurely, having slow convergence rate and so on. To solve above problems, a hybrid PSO algorithm based on chaos algorithm is brought forward. The hybrid PSO algorithm not only has the efficient parallelism but also increases the diversity of population because of the chaos algorithm. The simulation result shows that the new algorithm is prior to traditional PSO algorithm, having stronger adaptability and convergence, solving better the question on moving peaks benchmark.

scholarly journals Optimization of Gain, Impedance, and Bandwidth of Yagi-Uda Array Using Particle Swarm Optimization

2008 ◽  
Vol 2008 ◽  
pp. 1-4 ◽  
Author(s):  
Munish Rattan ◽  
Manjeet Singh Patterh ◽  
B. S. Sohi
Keyword(s):  
Particle Swarm Optimization ◽  
Method Of Moments ◽  
High Performance ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Computational Cost ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Evolutionary Technique ◽  
Global Optimization Technique

Particle swarm optimization (PSO) is a new, high-performance evolutionary technique, which has recently been used for optimization problems in antennas and electromagnetics. It is a global optimization technique-like genetic algorithm (GA) but has less computational cost compared to GA. In this paper, PSO has been used to optimize the gain, impedance, and bandwidth of Yagi-Uda array. To evaluate the performance of designs, a method of moments code NEC2 has been used. The results are comparable to those obtained using GA.

Economic emission dispatch using an advanced particle swarm optimization technique

2019 ◽  
Vol 16 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Hamid Rezaie ◽  
Mehrdad Abedi ◽  
Saeed Rastegar ◽  
Hassan Rastegar
Keyword(s):  
Particle Swarm Optimization ◽  
Power Systems ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Content Type ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Purpose This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

scholarly journals Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
J. J. Jamian ◽  
M. N. Abdullah ◽  
H. Mokhlis ◽  
M. W. Mustafa ◽  
A. H. A. Bakar
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
High Dimensional ◽  
Local Optimum ◽  
Mathematical Function ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Particle Position

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

Particle Swarm Optimization from Theory to Applications

2018 ◽  
Vol 5 (2) ◽  
pp. 1-24 ◽  
Author(s):  
M.A. El-Shorbagy ◽  
Aboul Ella Hassanien
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Population Based ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Unconstrained Optimization Problems

Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence. PSO is related to the study of swarms; where it is a simulation of bird flocks. It can be used to solve a wide variety of optimization problems such as unconstrained optimization problems, constrained optimization problems, nonlinear programming, multi-objective optimization, stochastic programming and combinatorial optimization problems. PSO has been presented in the literature and applied successfully in real life applications. In this paper, a comprehensive review of PSO as a well-known population-based optimization technique. The review starts by a brief introduction to the behavior of the PSO, then basic concepts and development of PSO are discussed, it's followed by the discussion of PSO inertia weight and constriction factor as well as issues related to parameter setting, selection and tuning, dynamic environments, and hybridization. Also, we introduced the other representation, convergence properties and the applications of PSO. Finally, conclusions and discussion are presented. Limitations to be addressed and the directions of research in the future are identified, and an extensive bibliography is also included.

Aerodynamic Shape Optimization Using Improved Territorial Particle Swarm Algorithm

2012 ◽  
Author(s):  
Amir Nejat ◽  
Pooya Mirzabeygi ◽  
Masoud Shariat-Panahi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Weighted Average ◽  
Particle Swarm Algorithm ◽  
Density Estimator ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective

In this paper, a new robust optimization technique with the ability of solving single and multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is an improved Territorial Particle Swarm Optimization (TPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil design obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in high angle of attack conditions. The optimized airfoil also reveals an evident improvement over the test case airfoil across all objective functions presented.

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