scholarly journals Steady state particle swarm

2019 ◽  
Vol 5 ◽  
pp. e202 ◽  
Author(s):  
Carlos M. Fernandes ◽  
Nuno Fachada ◽  
Juan-Julián Merelo ◽  
Agostinho C. Rosa
Keyword(s):  
Steady State ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Step ◽  
Solution Vector ◽  
Interacting Species ◽  
Dynamic Update ◽  
Update Strategy

This paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.

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.

scholarly journals A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

2019 ◽  
Author(s):  
Łukasz Strąk ◽  
Rafał Skinderowicz ◽  
Urszula Boryczka ◽  
Arkadiusz Nowakowski
Keyword(s):  
Travelling Salesman Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Discrete Particle Swarm Optimization ◽  
Travelling Salesman ◽  
Discrete Pso ◽  
Parameter Values ◽  
Positive Effect ◽  
Uniform Parameter

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

A Modified Particle Swarm Optimization Algorithm with Cases Studies

2011 ◽  
Vol 268-270 ◽  
pp. 823-828
Author(s):  
Cheng Chien Kuo ◽  
Hung Cheng Chen ◽  
Teng Fa Taso ◽  
Chin Ming Chiang
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Fast Calculation ◽  
Modified Particle Swarm Optimization ◽  
Stochastic Optimization Algorithms

s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made from different algorithms are then compared between the quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the simulation results, SA-PSO obtained higher efficiency, better quality and faster convergence speed than other compared algorithms.

scholarly journals Improving the Quality of Routing Service using Metaheuristic PSO algorithm in VANET Networks

2021 ◽  
Author(s):  
Amir Javadpour ◽  
Samira Rezaei ◽  
Arun Kumar Sangaiah ◽  
Adam Slowik ◽  
Shadi Mahmoodi Khaniabadi
Keyword(s):  
Particle Swarm Optimization ◽  
Information Quality ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Comparison Results

Abstract VANETs are organized to progress road protection with no specific need for any fixed infrastructure. Subsequently, the movement of all vehicles can be planned in the upcoming future, based on perceived information, Quality of Services Routing (QoSR) algorithms can be pressured on its available options, paths, and links and according to criteria and reliability of the QoSR. Awareness of QoSR to the environmental conditions of the network of vehicles, such as the location of vehicles, direction and speed that can be obtained. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path / link. In this article, A QoSR with Particle Swarm Optimization (QoSR-PSO) for improving QoSs in vehicular ad-hoc networks has been used. The particle swarm optimization algorithm by modeling the behavior of a set of particles looks for the optimal solution of the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop and overload.

EVOLVING THE UPDATE STRATEGY OF THE PARTICLE SWARM OPTIMISATION ALGORITHMS

2007 ◽  
Vol 16 (01) ◽  
pp. 87-109
Author(s):  
LAURA DIOŞAN ◽  
MIHAI OLTEAN
Keyword(s):  
Particle Swarm ◽  
Pso Algorithm ◽  
Complex Model ◽  
Particle Swarm Optimisation ◽  
Hybrid Technique ◽  
Real World Problem ◽  
Different Dimensions ◽  
Update Strategy ◽  
Optimisation Algorithms ◽  
Better Than

A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions.

scholarly journals The Generalized PSO: A New Door to PSO Evolution

2008 ◽  
Vol 2008 ◽  
pp. 1-15 ◽  
Author(s):  
J. L. Fernández Martínez ◽  
E. García Gonzalo
Keyword(s):  
Inverse Problems ◽  
Global Minimum ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Time Step ◽  
Continuous Version ◽  
Stability Regions ◽  
Griewank Function ◽  
The Stability ◽  
Exploration Task

A generalized form of the particle swarm optimization (PSO) algorithm is presented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a time step different than the unit. Generalized continuous particle swarm optimizations are compared in terms of attenuation and oscillation. The deterministic and stochastic stability regions and their respective asymptotic velocities of convergence are analyzed as a function of the time step and the GPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect of stochasticity on the stability of trajectories. The stability regions for the second-, third-, and fourth-order moments depend on inertia, local, and global accelerations and the time step and are inside of the deterministic stability region for the same time step. We prove that stability regions are the same under stagnation and with a moving center of attraction. Properties of the second-order moments variance and covariance serve to propose some promising parameter sets. High variance and temporal uncorrelation improve the exploration task while solving ill-posed inverse problems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments using well-known benchmark functions with two types of ill-posedness commonly found in inverse problems: the Rosenbrock and the “elongated” DeJong functions (global minimum located in a very flat area), and the Griewank function (global minimum surrounded by multiple minima). Numerical simulations support the results provided by theoretical analysis. Based on these results, two variants of Generalized PSO algorithm are proposed, improving the convergence and the exploration task while solving real applications of inverse problems.

An efficient particle swarm optimization with homotopy strategy for global numerical optimization

2021 ◽  
pp. 1-15
Author(s):  
Zhaojun Zhang ◽  
Xuanyu Li ◽  
Shengyang Luan ◽  
Zhaoxiong Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Prior Knowledge ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Initial Population ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Homotopy Analysis

Particle swarm optimization (PSO) as a successful optimization algorithm is widely used in many practical applications due to its advantages in fast convergence speed and convenient implementation. As a population optimization algorithm, the quality of initial population plays an important role in the performance of PSO. However, random initialization is used in population initialization for PSO. Using the solution of the solved problem as prior knowledge will help to improve the quality of the initial population solution. In this paper, we use homotopy analysis method (HAM) to build a bridge between the solved problems and the problems to be solved. Therefore, an improved PSO framework based on HAM, called HAM-PSO, is proposed. The framework of HAM-PSO includes four main processes. It contains obtaining the prior knowledge, constructing homotopy function, generating initial solution and solving the to be solved by PSO. In fact, the framework does not change the particle swarm optimization algorithm, but replaces the random population initialization. The basic PSO algorithm and three others typical PSO algorithms are used to verify the feasibility and effectiveness of this framework. The experimental results show that the four PSO using this framework are better than those without this framework.

Image Segmentation Research Based on Particle Swarm Optimization

2011 ◽  
Vol 403-408 ◽  
pp. 1644-1647
Author(s):  
Xia Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Feature Representation ◽  
Swarm Optimization ◽  
Representation Method ◽  
Optimal Feature ◽  
Image Segmentation Algorithm

As one of the difficulties and hot of computer vision and image processing, Image segmentation is highly valued by the research workers. Yet there is no image segmentation algorithm which is generic, and it is difficult to obtain an optimal feature representation method. In this paper, particle swarm optimization has proposed to segment the image. PSO algorithm can improve the efficiency and quality of the picture some extent through the experimental results. The algorithm has some versatility, as long as the corresponding parameters are adjusted, it can also handle the other images. The results show that PSO algorithm is very stable, and the fusion result is more satisfactory.

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