An Improved Convergence Particle Swarm Optimization Algorithm with Random Sampling of Control Parameters

Sun, Lijun; Song, Xiaodong; Chen, Tianfei

doi:10.1155/2019/7478498

Cited by 30 publications

(14 citation statements)

References 24 publications

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“…Also in order to utilize dimension information of particles with better velocity and position, the stochastic correction method is assumed on each dimension for the population prime value. The experimental results have shown that the method suggested improves the rate of convergence with increase in the accuracy of convergence as compared to basic PSO [5]…”

Section: ) Improved Convergence Pso With Random Sampling Mechanismmentioning

confidence: 99%

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Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

Handur¹

2021

TURCOMAT

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Development of technology like Cloud Computing and its widespread usage has given rise to exponential increase in the volume of traffic. With this increase in huge traffic the resources in the network would either be insufficient to handle the traffic or the situation may cause some of the resources to be over utilized or underutilized. This condition leads to reduced performance of the system. To improve the performance of the system the traffic requires to be regulated such that all the resources are utilized conferring to their capacity which is known as load balancing. Load balancing has been one of the concerns in the distributed computing systems where the computing nodes do not have a global view of the network. There have been constant efforts to provide an efficient solution for load balancing through the approaches like game theory, fuzzy logic, heuristics and metaheuristics. Even though various solutions exist for balancing the load, the issue is challenging as there does not exist one best fit solution. The paper aims at the study of how Particle Swarm Optimization approach is used to achieve an optimal solution for load balancing in distributed computing system.

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Section: ) Improved Convergence Pso With Random Sampling Mechanismmentioning

confidence: 99%

“…This section presents the basic principle of PSO, its variants in the research for achieving load balancing according to [5]-[22]…”

Section: Study Of Particle Swarm Optimizationmentioning

confidence: 99%

Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

Handur¹

2021

TURCOMAT

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“…In pursuit of a better coverage technique, a majority of scholars have tried to use intelligent algorithms, like Genetic Algorithm (GA) [18] and Particle Swarm Optimization (PSO) [19], to solve the issue. Though the Fruit Fly Optimization Algorithm is more simple and practicable than GA and PSO, but due to unavoidable limitations, the researchers are still exerting their efforts to develop a shrewder algorithm.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

Ashraf

Alfandi

Ahmad³

et al. 2020

Wireless Communications and Mobile Computing

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Due to unavoidable environmental factors, wireless sensor networks are facing numerous tribulations regarding network coverage. These arose due to the uncouth deployment of the sensor nodes in the wireless coverage area that ultimately degrades the performance and confines the coverage range. In order to enhance the network coverage range, an instance (node) redeployment-based Bodacious-instance Coverage Mechanism (BiCM) is proposed. The proposed mechanism creates new instance positions in the coverage area. It operates in two stages; in the first stage, it locates the intended instance position through the Dissimilitude Enhancement Scheme (DES) and moves the instance to a new position, while the second stage is called the depuration, when the moving distance between the initial and intended instance positions is sagaciously reduced. Further, the variations of various parameters of BiCM such as loudness, pulse emission rate, maximum frequency, grid points, and sensing radius have been explored, and the optimized parameters are identified. The performance metric has been meticulously analyzed through simulation results and is compared with the state-of-the-art Fruit Fly Optimization Algorithm (FOA) and, one step above, the tuned BiCM algorithm in terms of mean coverage rate, computation time, and standard deviation. The coverage range curve for various numbers of iterations and sensor nodes is also presented for the tuned Bodacious-instance Coverage Mechanism (tuned BiCM), BiCM, and FOA. The performance metrics generated by the simulation have vouched for the effectiveness of tuned BiCM as it achieved more coverage range than BiCM and FOA.

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“…It can either be seen either as PSO with developing loads or as a transformative calculation with a development rule acquired from PSO. EPSO has just demonstrated to be proficient, precise and hearty, along these lines appropriate to power system issues [9][10].…”

Section: Evolutionary Particle Swarm Optimizationmentioning

confidence: 99%

A Novel way out to Unit Commitment Problem utilizing Evolutionary Particle Swarm Optimization

Chinda

2019

IJRTE

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This paper differentiates the shows of three Unit Commitment strategies, three of which are the essential arrangement techniques for taking care of the Unit Commitment Problem named Priority List and Dynamic Programming strategies. The third strategy is the Evolutionary Particle Swarm Optimization method which has been applied productively to a plentiful blend of streamlining issues. Various regions in control frameworks require understanding at least one nonlinear streamlining emergencies. In spite of the way that systematic techniques may experience moderate intermingling and the scourge of dimensionality, heuristics-based swarm knowledge can be a capable substitute. Evolutionary Particle Swarm Optimization (EPSO), some portion of the swarm insight family, is known to adequately take care of enormous scale nonlinear improvement issues. This paper introduces the exit plan for Unit Commitment Problem by methods for EPSO system. A calculation was created to achieve an exit plan to the Unit Commitment Problem utilizing EPSO procedure. The adequacy of the calculation is tried on three generating units and the cultivated results utilizing the three techniques are thought about for complete working expense.

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An Improved Convergence Particle Swarm Optimization Algorithm with Random Sampling of Control Parameters

Cited by 30 publications

References 24 publications

Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

Particle Swarm Optimization for Load Balancing in Distributed Computing Systems – A Survey

Bodacious-Instance Coverage Mechanism for Wireless Sensor Network

A Novel way out to Unit Commitment Problem utilizing Evolutionary Particle Swarm Optimization

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