Using particle swarm optimization algorithm to optimally locating and controlling of pressure reducing valves for leakage minimization in water distribution systems

Jafari‐Asl, Jafar; Kashkooli, Bahram Sami; Bahrami, Mehdi

doi:10.1007/s40899-020-00426-3

Cited by 26 publications

(13 citation statements)

References 24 publications

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“…The elite learning strategy is determined by the elite learning rate σ. The calculation formula of σ is shown in Equation (6).…”

Section: Elite Learning Strategymentioning

confidence: 99%

“…Metaheuristic algorithms are algorithms inspired by the life habits of various creatures in nature. Metaheuristic algorithms can effectively solve many problems in life [1] and are widely used in finance, transportation, physics, chemistry, military, and other fields [2][3][4][5][6][7][8]. The No Free Lunch Theorem [9,10] proves that any optimization algorithm cannot suit all situations.…”

Section: Introductionmentioning

confidence: 99%

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A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

Zheng

Liu

Chai

et al. 2021

Wireless Communications and Mobile Computing

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The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

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“…The elite learning strategy is determined by the elite learning rate σ. The calculation formula of σ is shown in Equation (6).…”

Section: Elite Learning Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

Zheng

Liu

Chai

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Among these methods, PSO is famous for fast convergence and easy applying. PSO algorithms are widely applied in engineering like route planning [ 8 , 9 ], data clustering [ 10 , 11 ], feature selection [ 12 , 13 ], image segmentation [ 14 , 15 ], power system [ 16 , 17 ], engineering areas [ 18 – 20 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

et al. 2022

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An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

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“…The inspiration source of heuristic algorithm can be divided into many kinds, including (1) The swarm intelligence algorithms that imitates the behavior of animals in nature. The classical algorithm includes particle swarm optimization (PSO) [ 9 ], quantum-based avian navigation optimizer algorithm (QANA) [ 10 ], dragonfly algorithm (DA) [ 11 ]; (2) Simulating physical process, classical algorithms include simulated annealing (SA) [ 12 ]; (3) Inspired biological evolution, including non-dominated sequencing genetic algorithm (NSGA-II) [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of water distribution networks based on non-dominated sequencing genetic algorithm

Yan

Yang

et al. 2022

PLoS ONE

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Due to the conflict between reducing cost and improving water supply performance, how to select the appropriate pipe diameter is a current challenge. In this paper, the problem is transformed into a multi-objective optimization problem, and the evolutionary genetic optimization algorithm is used to solve the problem to determine the optimal selection of pipe diameter in the pipe network. To solve this problem, the evolutionary genetic algorithm was coupled with EPANET hydraulic simulation software in Python environment. The results show that NSGA-II and NSGA-III perform better in two typical case tests. Moreover, the increase of the objective functions will lead to an increase in the amount of data in the optimal solution set, and will affect the optimal value of each objective function. That shows that the balance between the economy and reliability of water supply can be successfully found by coupling the hydraulic model and the multi-objective optimization algorithm, which can provide an auxiliary decision for enterprises.

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Using particle swarm optimization algorithm to optimally locating and controlling of pressure reducing valves for leakage minimization in water distribution systems

Cited by 26 publications

References 24 publications

A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

Multi-objective optimization of water distribution networks based on non-dominated sequencing genetic algorithm

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