Particle Swarm Optimization approach for waterjet cavitation peening

Improving Mobile Ad-Hoc Network (MANET) performance is a tedious task because of the dynamic and uncertain characteristics of the nodes. MANET nodes connected to multiple applications that involve a high exchange of data. To add reliability to the application services, MANETs optimized through dedicated load balancing schemes. The Dynamic Range Clustering (DRC) algorithm associated with Learning-based Routing (LR) used in the present work to improve the energy efficiency and the nature of the network's data handling. DRC design focuses on selecting the head of the cluster and maintaining stability over the cluster. LR selects unique neighbors that assist in ensuring the efficient, non-congested communication of data exchange. An effort has made to combine the two incompatible methods to increase the performance of the network over differential network traffic. The proposed approach tested using simulations and measures output by comparative analysis.

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“…(12) by solving Eq. (11). ∆δ = v t * q max (12) After the completion of the transmission process, the node is categorized as learning 1 or 2.…”

Section: Learning 2: Qmin≤qamentioning

confidence: 99%

“…In the load balancing process, workloads were distributed across the available nodes for overloading and resource exploitation [11]. Additive concern was the selection of appropriate nodes to ensure reliable forward-capacity data, avoiding packet loss and replication.…”

Section: Introductionmentioning

confidence: 99%

Improving the Energy Efficiency in Mobile Ad-Hoc Network Using Learning-Based Routing

Aroulanandam¹,

Latchoumi²,

Balamurugan³

et al. 2020

RIA

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“…Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems, in which the best solution can be represented as a point or surface in an n-dimensional space. This algorithm is one of the strongest population-based metaheuristic methods [30]. Hypotheses are formed in this space and seeded with an initial velocity, as well as a communication channel between the particles.…”

Section: B Pso-monte Carlo (Simulation-optimization)mentioning

confidence: 99%

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Salami

Sobhani

Ghazizadeh

2019

Applied Computer Systems

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Many real world problems have big data, including recorded fields and/or attributes. In such cases, data mining requires dimension reduction techniques because there are serious challenges facing conventional clustering methods in dealing with big data. The subspace selection method is one of the most important dimension reduction techniques. In such methods, a selected set of subspaces is substituted for the general dataset of the problem and clustering is done using this set. This article introduces the Shared Subscribe Hyper Simulation Optimization (SUBHSO) algorithm to introduce the optimized cluster centres to a set of subspaces. SUBHSO uses an optimization loop for modifying and optimizing the coordinates of the cluster centres with the particle swarm optimization (PSO) and the fitness function calculation using the Monte Carlo simulation. The case study on the big data of Iran electricity market (IEM) has shown the improvement of the defined fitness function, which represents the cluster cohesion and separation relative to other dimension reduction algorithms.

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“…As was discussed, only a few researches have been carried out in the second category of this research using effective methods such as meta‐heuristic methods. Therefore, ABC, PSO and their improved versions (IPSO and IABC) are used here to minimize the construction cost of stepped spillway due to the unique characteristics of these algorithms in which they were presented in different papers to solve various large‐scale engineering problems (Garg, 2016; Patwal et al , 2018; Cao et al , 2019; Latchoumi et al, 2019; Fang and Popole, 2019; Latif and Saka, 2019; Mann and Singh, 2019; Moeini and Soghrati, 2019, Xin et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Construction cost minimisation of the stepped spillway using improved particle swarm optimisation and artificial bee colony algorithms

Jazayeri

Moeini

2020

Water & Environment J

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In this paper, the improved artificial bee colony (IABC) and improved particle swarm optimisation (IPSO) have been used for the optimal design of stepped spillway with the minimisation of construction cost as the energy dissipater structure for a dam project. Generally, the construction cost of this structure is the most expensive part of the project. However, traditional designing methods of stepped spillway such as the Vittal and Porey (VP) approach cannot find optimal dimensions of the stepped spillway with minimum cost. Therefore, the optimisation methods such as meta‐heuristic algorithms have been used. As a case study, Tehri dam’s stepped spillway in India has been considered here and the optimal dimensions of this spillway have been obtained using IPSO, IABC algorithms and the results have been compared with VP approach and other available results. Comparison of the results shows that when the three‐stepped spillway is considered, the results of IABC and IPSO algorithms are improved by 17.72% in comparison with VP results. In addition, when the four‐stepped spillway is considered, the results of IABC, IPSO algorithms are, respectively, improved by 16.47% and 16.53% in comparison with the VP results.

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Particle Swarm Optimization approach for waterjet cavitation peening

Cited by 58 publications

References 29 publications

Improving the Energy Efficiency in Mobile Ad-Hoc Network Using Learning-Based Routing

Improving the Energy Efficiency in Mobile Ad-Hoc Network Using Learning-Based Routing

Shared Subscribe Hyper Simulation Optimization (SUBHSO) Algorithm for Clustering Big Data – Using Big Databases of Iran Electricity Market

Construction cost minimisation of the stepped spillway using improved particle swarm optimisation and artificial bee colony algorithms

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