MOVPSO: Vortex Multi-Objective Particle Swarm Optimization

Meza, Joaquín; Espitia, Helbert Eduardo; Montenegro, Carlos; Giménez, Elena; Crespo, Rubén González

doi:10.1016/j.asoc.2016.09.026

Cited by 67 publications

(21 citation statements)

References 21 publications

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“…Each bird in the flock moves towards best position with velocity dependent on the current position of the bird and then, they explore the search space from their new positions. The process is reiterated until they reach their destination [45].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Kaur¹,

Arora²

2017

IJIMAI

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“…The location should be expected to be the one where the transportation is the most convenient. This is exactly what particle swarm optimization (PSO) can do [33].…”

Section: The Infrastructure Convenience Indexmentioning

confidence: 91%

A numerical-analysis-based optimization method for location selection for planning residential areas in grid transportation networks

et al. 2017

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China's economy has been rapidly growing over the past few decades; however, increasingly more modern cities are facing problems caused by poor city planning, such as traffic networks, resource distribution and routine management. Optimization methods are urgently needed during city development. From location planning perspective, the residential area issue is discussed in this paper. Based on this issue, a numerical-analysis-based method is proposed for selecting proper residential areas in grid transportation networks. First, a grid layout is introduced to formalize a real traffic network. Furthermore, to guarantee its approximation, a considerable amount of data are obtained from real-world scenarios based on the shortest routes to demonstrate the problem of traffic jams. Then, a quantitative evaluation system is proposed, which quantitatively evaluates the specified location with the traffic flow distribution index, traffic congestion index and infrastructure convenience index. Each index reflects the attitude of citizens from a unique perspective, which affects the final location planning and selection. Finally, an analytic hierarchy process is designed to analyse all these indices together for a comprehensive analysis, and case studies and experiments are conducted to demonstrate the effectiveness of the proposed method. The generated residential location can be considered as a technical reference for further city planning and development decisions.

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“…None of the work has been reported by researchers yet to optimize the S-shaped duct using a PSO algorithm. The PSO algorithm allows individual populations to benefit from their past experiences that ultimately contribute to the faster convergence rate than other evolutionary algorithms [27][28][29][30][31][32][33][34]. Many times, computational time taken by PSO for the same number of function evaluations is quite less than other heuristic algorithms [25,35].…”

Section: Introductionmentioning

confidence: 99%

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

Sharma

Baloni

2020

SN Appl. Sci.

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A high-bypass turbofan engine transfers air from low-to the high-pressure compressor through an S-shaped transition duct. Minimization of the total pressure loss and maximization of uniform flow are key factors to ensure the maximum performance of the S-shaped transition duct. The conventional design approach is time-consuming and does not guarantee an optimal solution. Hence, the present article is based on the application of optimization for the S-shaped compressor transition duct. The optimization is carried out on the basis of shape parameters to minimize objectives namely pressure loss coefficient and non-uniformity with the reduction of length of the S-shaped transition duct. The 2-D axisymmetric approach of computational fluid dynamics is used as a design tool for performance evaluation with optimization techniques. The simulation model is validated with the available experimental results of the literature. The correlation between the response and independent variables is established with the help of the second-order polynomial response surface methodology. Further, individual optimization is carried out for single-objective consideration using particle swarm optimization and gray wolf optimization algorithms. As the results of single-objective optimization depict the conflicts between both objectives, later optimization using multi-objective particle swarm optimization and multi-objective gray wolf optimization algorithms is carried out. The performance metrics are obtained and compared. The 'TOPSIS' method is applied to get best solution out of multiple solutions. The optimized duct reduced pressure loss coefficient and non-uniformity index by 28.14% and 43.33% with a reduction of 6.37% of length compared to baseline S-shaped duct.

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MOVPSO: Vortex Multi-Objective Particle Swarm Optimization

Cited by 67 publications

References 21 publications

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

A numerical-analysis-based optimization method for location selection for planning residential areas in grid transportation networks

Design optimization of S-shaped compressor transition duct using particle swarm optimization algorithm

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