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.

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.

scholarly journals On the optimal control of single-stage hybrid manufacturing systems via novel and different variants of particle swarm optimization algorithm

2005 ◽  
Vol 2005 (3) ◽  
pp. 257-279 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Manufacturing Systems ◽  
Hypothesis Test ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight

This paper presents several novel approaches of particle swarm optimization (PSO) algorithm with new particle velocity equations and three variants of inertia weight to solve the optimal control problem of a class of hybrid systems, which are motivated by the structure of manufacturing environments that integrate process and optimal control. In the proposed PSO algorithm, the particle velocities are conceptualized with the local best (orpbest) and global best (orgbest) of the swarm, which makes a quick decision to direct the search towards the optimal (fitness) solution. The inertia weight of the proposed methods is also described as a function of pbest and gbest, which allows the PSO to converge faster with accuracy. A typical numerical example of the optimal control problem is included to analyse the efficacy and validity of the proposed algorithms. Several statistical analyses including hypothesis test are done to compare the validity of the proposed algorithms with the existing PSO technique, which adopts linearly decreasing inertia weight. The results clearly demonstrate that the proposed PSO approaches not only improve the quality but also are more efficient in converging to the optimal value faster.

scholarly journals On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

2006 ◽  
Vol 2006 ◽  
pp. 1-17 ◽  
Author(s):  
M. Senthil Arumugam ◽  
M. V. C. Rao
Keyword(s):  
Optimal Control ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Single Stage ◽  
Hybrid Manufacturing ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Real Coded Genetic Algorithm ◽  
The Impact

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. 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 Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

Runtime analysis of discrete particle swarm optimization algorithms: A survey

2019 ◽  
Vol 61 (4) ◽  
pp. 177-185
Author(s):  
Moritz Mühlenthaler ◽  
Alexander Raß
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Upper And Lower Bounds ◽  
Swarm Optimization ◽  
Randomized Search Heuristics ◽  
Recent Developments ◽  
Randomized Search

Abstract A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.

scholarly journals An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 357 ◽  
Author(s):  
Shu-Kai S. Fan ◽  
Chih-Hung Jen
Keyword(s):  
Particle Swarm Optimization ◽  
Unconstrained Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Searching Strategy ◽  
Wide Range

Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.

scholarly journals Improved Adaptive Holonic Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Hao Li ◽  
Hongbin Jin ◽  
Hanzhong Wang ◽  
Yanyan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Swarm Optimization ◽  
State Switching ◽  
Grouping Strategy ◽  
Number Of Iterations

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

Application of Quantum-Behaved Particle Swarm Optimization in Engineering Constrained Optimization Problems

2011 ◽  
Vol 383-390 ◽  
pp. 7208-7213
Author(s):  
De Kun Tan
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Standard Particle Swarm Optimization ◽  
Fixed Path

To overcome the shortage of standard Particle Swarm Optimization(SPSO) on premature convergence, Quantum-behaved Particle Swarm Optimization (QPSO) is presented to solve engineering constrained optimization problem. QPSO algorithm is a novel PSO algorithm model in terms of quantum mechanics. The model is based on Delta potential, and we think the particle has the behavior of quanta. Because the particle doesn’t have a certain trajectory, it has more randomicity than the particle which has fixed path in PSO, thus the QPSO more easily escapes from local optima, and has more capability to seek the global optimal solution. In the period of iterative optimization, outside point method is used to deal with those particles that violate the constraints. Furthermore, compared with other intelligent algorithms, the QPSO is verified by two instances of engineering constrained optimization, experimental results indicate that the algorithm performs better in terms of accuracy and robustness.

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 Algoritme Particle Swarm Optimization (PSO) untuk Optimasi Perencanaan Produksi Agregat Multi-Site pada Industri Tekstil Rumahan

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Agung Mustika Rizki ◽  
Afina Lina Nurlaili
Keyword(s):  
Particle Swarm Optimization ◽  
Production Planning ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Efficient Solutions ◽  
Production Costs ◽  
Aggregate Production Planning ◽  
Swarm Optimization ◽  
Aggregate Production

In the industrial world, companies need to manage their production areas well. One way is to implement aggregate production planning. The goal is that the production costs incurred by the company can be controlled properly. However, production planning cannot be formulated quickly. The problem is more complicated if the company has several production locations. The difference in location also affects the production references and standards applied in each location. Based on these problems, the authors propose to apply the Particle Swarm Optimization (PSO) algorithm to solve the problem of aggregate production planning in order to obtain the optimal solution for each production location. As a result, the algorithm proposed by the author can produce optimal and efficient solutions for 6 production sites. This is evidenced by the relatively short time required compared to the previous planning by the company.

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