A Parellelzing Modified Particle Swarm Optimizer and its Application to Discrete Topological Optimization

2012 ◽  
Vol 433-440 ◽  
pp. 4401-4408
Author(s):  
Bin Yang ◽  
Qi Lin Zhang
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Topological Optimization ◽  
Particle Swarm Optimizer ◽  
Structural Problems ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Speed Up ◽  
Truss Topological Optimization

recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems and competitive results are obtained. In order to reduce its execution time for solving large complex optimization problem, a parallel version for this optimizer (PMLPSO) is studied in this paper. This paper first gives an overview of PSO algorithm as well as the modified PSO, and then a design and an implementation of parallel PSO is proposed. Since most of structural problems involve discrete design variables, an effect strategy is involved in MLPSO in order to operate on discrete variables. The performance of the proposed algorithm is tested by two examples and promising speed-up rate is obtained. Final part is conclusion and outlook.

Parallelizing a Modified Particle Swarm Optimizer (PSO)

2010 ◽  
Vol 163-167 ◽  
pp. 2404-2409 ◽  
Author(s):  
Bin Yang ◽  
Qi Lin Zhang
Keyword(s):  
Execution Time ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Topological Optimization ◽  
Particle Swarm Optimizer ◽  
Speed Up ◽  
Truss Topological Optimization ◽  
High Computational Cost

Recently, a modified Particle Swarm Optimizer (MLPSO) has been succeeded in solving truss topological optimization problems and competitive results are obtained. Since this optimizer belongs to evolutionary algorithm and plagued by high computational cost as measured by execution time, in order to reduce its execution time for solving large complex optimization problem, a parallel version for this optimizer is studied in this paper. This paper first gives an overview of PSO algorithm as well as the modified PSO, and then a design and an implementation of parallel PSO is proposed. The performance of the proposed algorithm is tested by two examples and promising speed-up rate is obtained. Final part is conclusion and outlook.

Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

2015 ◽  
Vol 24 (05) ◽  
pp. 1550017 ◽  
Author(s):  
Aderemi Oluyinka Adewumi ◽  
Akugbe Martins Arasomwan
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Search Range ◽  
Original Algorithm ◽  
Inertia Weight ◽  
And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

A simple and efficient constrained particle swarm optimization and its application to engineering design problems

2010 ◽  
Vol 224 (2) ◽  
pp. 389-400 ◽  
Author(s):  
T-H Kim ◽  
I Maruta ◽  
T Sugie
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
Continuous Type ◽  
Design Variables ◽  
Engineering Design Problems

Engineering optimization problems usually contain various constraints and mixed integer-discrete-continuous type of design variables. This article proposes an efficient particle swarm optimization (PSO) algorithm for such problems. First, the constrained optimization problem is transformed into an unconstrained problem without introducing any problem-dependent or user-defined parameters such as penalty factors or Lagrange multipliers, though such parameters are usually required in general optimization algorithms. Then, the above PSO method is extended to handle integer, discrete, and continuous design variables in a simple manner, yet with a high degree of precision. The proposed PSO scheme is fairly simple and thus it is easy to implement. In order to demonstrate the effectiveness of our method, several mechanical design optimization problems are solved, and the numerical results are compared with those reported in the literature.

scholarly journals WEIGHT MINIMIZATION OF TRUSS STRUCTURES WITH SIZING AND LAYOUT VARIABLES USING INTEGRATED PARTICLE SWARM OPTIMIZER

2017 ◽  
Vol 23 (8) ◽  
pp. 985-1001 ◽  
Author(s):  
Ali MORTAZAVI ◽  
Vedat TOĞAN ◽  
Ayhan NUHOĞLU
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Truss Structures ◽  
Continuous Variables ◽  
Particle Swarm Optimizer ◽  
Search Spaces ◽  
Optimization Constraints ◽  
Weight Minimization ◽  
Ipso Algorithm

This study investigates the performances of the integrated particle swarm optimizer (iPSO) algorithm in the layout and sizing optimization of truss structures. The iPSO enhances the standard PSO algorithm employing both the concept of weighted particle and the improved fly-back method to handle optimization constraints. The performance of the recent algorithm is tested on a series of well-known truss structures weight minimization problems including mixed design search spaces (i.e. with both discrete and continuous variables) over various types of constraints (i.e. nodal dis­placements, element stresses and buckling criterion). The results demonstrate the validity of the proposed approach in dealing with combined layout and size optimization problems.

An improved PSO algorithm for solving nonlinear programing problems with constrained conditions

2020 ◽  
pp. 2150001
Author(s):  
Wei-Der Chang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Pso Algorithm ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Design Variables ◽  
Constrained Conditions ◽  
Engineering Optimization Problems

Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions.

scholarly journals A Brief Review of Firefly Algorithm: Application in Structural Optimization Problem

2019 ◽  
Vol 15 (2) ◽  
pp. 183-191
Author(s):  
Sujan Tripathi
Keyword(s):  
Particle Swarm Optimization ◽  
Structural Engineering ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Research Area ◽  
Swarm Optimization ◽  
Design Variables ◽  
Discrete Design Variables

 Firefly Algorithm is a recently developed meta-heuristic algorithm, which is inspired by the flashing behaviour of Firefly. Initially, Firefly algorithm was used to solve the optimization problems of continuous search domain. Further, many researchers have successfully implemented this algorithm in several discrete optimization problems. Although the firefly algorithm behaves like another meta-heuristic method (i.e. Particle Swarm Optimization particle), however, the firefly is robust than that. Due to the presence of an exponential term in its movement equation, firefly algorithm is capable to search optimum value more efficiently than others. This study is, mainly, focused to show the strength of the firefly algorithm to solve the complex problems and to explore the possible research area on the structural engineering field. This study shows about the robustness of the firefly algorithm on the basis of recently published papers that was used to solve the size, shape and topology optimization of the spatial truss structure with discrete design variables. The review result shows that the performance of the Firefly Algorithm is remarkable compared to other nature-inspired-algorithms, such as particle swarm optimization. This study concludes with some remarkable points that will be more beneficial to the future researchers of this area.

scholarly journals Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

2021 ◽  
Vol 11 (2) ◽  
pp. 839
Author(s):  
Shaofei Sun ◽  
Hongxin Zhang ◽  
Xiaotong Cui ◽  
Liang Dong ◽  
Muhammad Saad Khan ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Communication Systems ◽  
Optimization Problems ◽  
Classical Method ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Electromagnetic Analysis ◽  
Swarm Optimization ◽  
Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

scholarly journals Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

2021 ◽  
Vol 21 (1) ◽  
pp. 62-72
Author(s):  
R. B. Madhumala ◽  
Harshvardhan Tiwari ◽  
Verma C. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Virtual Machine Placement ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Cloud Datacenter

Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

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