GENETIC ALGORITHM VERSUS PARTICLE SWARM OPTIMIZATION IN N-QUEEN PROBLEM

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

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A Modified NM-PSO Method for Parameter Estimation Problems of Models

Journal of Applied Mathematics ◽

10.1155/2012/530139 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

An Liu ◽

Erwie Zahara ◽

Ming-Ta Yang

Keyword(s):

Genetic Algorithm ◽

Parameter Estimation ◽

Particle Swarm Optimization ◽

Differential Equations ◽

Ordinary Differential Equations ◽

Optimization Problems ◽

Particle Swarm ◽

Swarm Optimization ◽

Wide Range ◽

Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

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Adaptive resource allocation based on modified Genetic Algorithm and Particle Swarm Optimization for multiuser OFDM systems

2008 International Conference on Electrical and Computer Engineering ◽

10.1109/icece.2008.4769202 ◽

2008 ◽

Cited By ~ 20

Author(s):

Imtiaz Ahmed ◽

Satya Prasad Majumder

Keyword(s):

Genetic Algorithm ◽

Resource Allocation ◽

Particle Swarm Optimization ◽

Particle Swarm ◽

Adaptive Resource Allocation ◽

Ofdm Systems ◽

Swarm Optimization ◽

Modified Genetic Algorithm ◽

Adaptive Resource ◽

Multiuser Ofdm

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Availability and performance optimization of urea decomposition system using genetic algorithm and particle swarm optimization

Life Cycle Reliability and Safety Engineering ◽

10.1007/s41872-021-00167-y ◽

2021 ◽

Author(s):

Monika Saini ◽

Yashpal Singh Raghav ◽

Ashish Kumar ◽

Divya Chandnani

Keyword(s):

Genetic Algorithm ◽

Particle Swarm Optimization ◽

Performance Optimization ◽

Particle Swarm ◽

Swarm Optimization ◽

And Performance ◽

Urea Decomposition ◽

Decomposition System

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Service Composition in IoT using Genetic algorithm and Particle swarm optimization

Open Computer Science ◽

10.1515/comp-2020-0011 ◽

2020 ◽

Vol 10 (1) ◽

pp. 56-64 ◽

Cited By ~ 1

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.

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