Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

2009 ◽  
Vol 223 (5) ◽  
pp. 485-497 ◽  
Author(s):  
Y W Guo ◽  
A R Mileham ◽  
G W Owen ◽  
P G Maropoulos ◽  
W D Li
Keyword(s):  
Particle Swarm Optimization ◽  
Process Planning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Operation Sequencing ◽  
Swarm Optimization ◽  
Process Plan ◽  
Five Axis ◽  
Modified Pso

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles ‘fly’ intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

scholarly journals On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Terminal Point ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

An Improved Particle Swarm Optimization Algorithm for Global Multidimensional Optimization

2017 ◽  
Vol 29 (1) ◽  
pp. 127-142
Author(s):  
Rkia Fajr ◽  
Abdelaziz Bouroumi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Human Learning ◽  
Computing Methods ◽  
Swarm Optimization ◽  
Starting Point ◽  
New Variant

Abstract This paper introduces a new variant of the particle swarm optimization (PSO) algorithm, designed for global optimization of multidimensional functions. The goal of this variant, called ImPSO, is to improve the exploration and exploitation abilities of the algorithm by introducing a new operation in the iterative search process. The use of this operation is governed by a stochastic rule that ensures either the exploration of new regions of the search space or the exploitation of good intermediate solutions. The proposed method is inspired by collaborative human learning and uses as a starting point a basic PSO variant with constriction factor and velocity clamping. Simulation results that show the ability of ImPSO to locate the global optima of multidimensional functions are presented for 10 well-know benchmark functions from CEC-2013 and CEC-2005. These results are compared with the PSO variant used as starting point, three other PSO variants, one of which is based on human learning strategies, and three alternative evolutionary computing methods.

A Particle Swarm Optimization with Variety Factor

2012 ◽  
Vol 239-240 ◽  
pp. 1291-1297 ◽  
Author(s):  
Hai Sheng Qin ◽  
Deng Yue Wei ◽  
Jun Hui Li ◽  
Lei Zhang ◽  
Yan Qiang Feng
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Property ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Previous Algorithm ◽  
Modified Pso

A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.

Circuit Design Based on Particle Swarm Optimization Algorithms

2011 ◽  
Vol 474-476 ◽  
pp. 1093-1098 ◽  
Author(s):  
Xue Song Yan ◽  
Qing Hua Wu ◽  
Cheng Yu Hu ◽  
Qing Zhong Liang
Keyword(s):  
Particle Swarm Optimization ◽  
Circuit Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Random Solution ◽  
Swarm Optimization ◽  
Fish Schools ◽  
Optimum Solution ◽  
Evolutionary Electronics ◽  
Modified Pso

This work investigates the application of Particle Swarm Optimization (PSO) algorithms in the field of evolutionary electronics. PSO was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. PSO achieves its optimum solution by starting from a group of random solution and then searching repeatedly. We propose the new means for designing electronic circuits and introduce the modified PSO algorithm. For the case studies this means has proved to be efficient, experiments show that we have better results.

scholarly journals Particle Swarm Optimization

2017 ◽  
pp. 614-635 ◽  
Author(s):  
Alaa Tharwat ◽  
Tarek Gaber ◽  
Aboul Ella Hassanien ◽  
Basem E. Elnaghi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Pso Algorithm ◽  
Search Space ◽  
Learning Problems ◽  
Swarm Optimization ◽  
Numerical Example

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

scholarly journals Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem

2019 ◽  
Vol 9 (12) ◽  
pp. 2440 ◽  
Author(s):  
Muhammad Salman Fakhar ◽  
Syed Abdul Rahman Kashif ◽  
Noor Ul Ain ◽  
Hafiz Zaheer Hussain ◽  
Akhtar Rasool ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Scheduling Problem ◽  
Short Term ◽  
Swarm Optimization ◽  
Hydrothermal Scheduling ◽  
Dynamic Search ◽  
Accelerated Particle Swarm Optimization

The Accelerated Particle Swarm Optimization (APSO) algorithm is an efficient and the easiest to implement variant of the famous Particle Swarm Optimization (PSO) algorithm. PSO and its variant APSO have been implemented on the famous Short-Term Hydrothermal Scheduling (STHTS) problem in recent research, and they have shown promising results. The APSO algorithm can be further modified to enhance its optimizing capability by deploying dynamic search space squeezing. This paper presents the implementation of the improved APSO algorithm that is based on dynamic search space squeezing, on the short-term hydrothermal scheduling problem. To give a quantitative comparison, a true statistical comparison based on comparing means is also presented to draw conclusions.

scholarly journals Design of Two-Dimensional Recursive Digital Filter Using Multi Particle Swarm Optimization Algorithm

2020 ◽  
Vol 53 (4) ◽  
pp. 559-566
Author(s):  
Lakhdar Kaddouri ◽  
Amel B.H. Adamou-Mitiche ◽  
Lahcene Mitiche
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Fast Convergence ◽  
Infinite Impulse Response ◽  
Two Dimensional ◽  
Swarm Optimization ◽  
Local Optima

Particle Swarm Optimization (PSO) is an evolutionary algorithm widely used in optimization problems. It is characterized by a fast convergence, which can lead the algorithm to stagnate in local optima. In the present paper, a new Multi-PSO algorithm for the design of two-dimensional infinite impulse response (IIR) filters is built. It is based on the standard PSO and uses a new initialization strategy. This strategy is relayed to two types of swarms: a principal and auxiliaries. To improve the performance of the algorithm, the search space is divided into several areas, which allows a best covering and leading to a better exploration in each zone separately. This solved the problem of fast convergence in standard PSO. The results obtained demonstrate the effectiveness of the Multi-PSO algorithm in the filter coefficients optimization.

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