Estimation of distribution algorithm enhanced particle swarm optimization for water distribution network optimization

2015 ◽  
Vol 10 (2) ◽  
pp. 341-351 ◽  
Author(s):  
Xuewei Qi ◽  
Ke Li ◽  
Walter D. Potter
Keyword(s):  
Particle Swarm Optimization ◽  
Network Optimization ◽  
Water Distribution ◽  
Distribution Network ◽  
Particle Swarm ◽  
Water Distribution Network ◽  
Estimation Of Distribution Algorithm ◽  
Swarm Optimization ◽  
Estimation Of Distribution ◽  
Enhanced Particle Swarm Optimization

Análise de transitórios hidráulicos decorrentes da operação otimizada de estações elevatórias

2020 ◽  
pp. 17-26
Author(s):  
Gustavo Meirelles ◽  
◽  
Aloysio Saliba ◽  
Jorge Tarqui ◽  
Edna Viana ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Water Distribution ◽  
Particle Swarm ◽  
Water Distribution Network ◽  
Pressure Variation ◽  
Swarm Optimization ◽  
Transient Model ◽  
Demand Pattern ◽  
Steady State Model ◽  
Hydraulic Equipment

Neste trabalho são avaliados os transitórios hidráulicos decorrentes da operação otimizada de uma estação elevatória de uma rede de distribuição de água e os procedimentos operacionais que podem reduzir este problema para assegurar a confiabilidade do sistema. A operação otimizada é obtida utilizando o algoritmo Particle Swarm Optimization (PSO) e simulações em regime permanente, considerando que as bombas estarão operando com sua velocidade de rotação nominal ou desligadas. Em seguida, as manobras de arranque e paragem definidas são utilizadas num modelo em regime transitório para avaliar as variações de pressão decorrentes da operação otimizada. Os resultados obtidos demonstram que as variações de pressão não são elevadas, mas que, a longo prazo, podem ser significativos na redução da vida útil dos equipamentos hidráulicos. Além disso, observou-se que a variação da demanda num modelo transitório pode causar erros significativos, sendo necessária uma modelação cautelosa neste aspeto. In this work, the hydraulic transients resulting from the optimized operation of a pumping station in a water distribution network are studied and operational procedures to reduce this problem and ensure the reliability of the system are evaluated. An optimal pumping scheduling is obtained using the Particle Swarm Optimization (PSO) and a steady state model considering pumps operating only at their nominal rotational speed or switched off. Then, the pumps schedules are used in a transient model to evaluate the pressure surges of the optimized operation. The results showed that the pressure variation is not high but can be relevant in the reduction of service life of the hydraulic equipment. In addition, it was observed that the demand pattern in the transient model can cause significant errors, and its modeling has to be carefully handled.

scholarly journals An Integrated Method Based on PSO and EDA for the Max-Cut Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Geng Lin ◽  
Jian Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Estimation Of Distribution Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Integrated Method ◽  
Benchmark Instances ◽  
Max Cut Problem ◽  
Local Search Procedure ◽  
Estimation Of Distribution

The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution algorithm (PSO-EDA) for solving the max-cut problem. The integrated algorithm overcomes the shortcomings of particle swarm optimization and estimation of distribution algorithm. To enhance the performance of the PSO-EDA, a fast local search procedure is applied. In addition, a path relinking procedure is developed to intensify the search. To evaluate the performance of PSO-EDA, extensive experiments were carried out on two sets of benchmark instances with 800 to 20000 vertices from the literature. Computational results and comparisons show that PSO-EDA significantly outperforms the existing PSO-based and EDA-based algorithms for the max-cut problem. Compared with other best performing algorithms, PSO-EDA is able to find very competitive results in terms of solution quality.

scholarly journals An Enhanced Multi-Objective Particle Swarm Optimization in Water Distribution Systems Design

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1334
Author(s):  
Mohamed R. Torkomany ◽  
Hassan Shokry Hassan ◽  
Amin Shoukry ◽  
Ahmed M. Abdelrazek ◽  
Mohamed Elkholy
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Particle Swarm ◽  
Large Network ◽  
Swarm Optimization ◽  
Original Algorithm ◽  
Multi Objective

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

scholarly journals Proportional-Integral Controllers Performance of a Grid-Connected Solar PV System with Particle Swarm Optimization and Ziegler–Nichols Tuning Method

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2516
Author(s):  
Klemen Deželak ◽  
Peter Bracinik ◽  
Klemen Sredenšek ◽  
Sebastijan Seme
Keyword(s):  
Particle Swarm Optimization ◽  
Distribution Network ◽  
Particle Swarm ◽  
Open Loop ◽  
Limited Area ◽  
Solar Pv ◽  
Pv System ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Tuning Method

This paper deals with photovoltaic (PV) power plant modeling and its integration into the medium-voltage distribution network. Apart from solar cells, a simulation model includes a boost converter, voltage-oriented controller and LCL filter. The main emphasis is given to the comparison of two optimization methods—particle swarm optimization (PSO) and the Ziegler–Nichols (ZN) tuning method, approaches that are used for the parameters of Proportional-Integral (PI) controllers determination. A PI controller is commonly used because of its performance, but it is limited in its effectiveness if there is a change in the parameters of the system. In our case, the aforementioned change is caused by switching the feeders of the distribution network from an open-loop to a closed-loop arrangement. The simulation results have claimed the superiority of the PSO algorithm, while the classical ZN tuning method is acceptable in a limited area of operation.

Optimal allocation of distributed energy storage in active distribution network via hybrid teaching learning and multi-objective particle swarm optimization algorithm

2021 ◽  
pp. 002072092098369
Author(s):  
Jijun Liu ◽  
Yuxin Bai ◽  
Yingfeng He
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Optimal Allocation ◽  
Storage System ◽  
Distribution Network ◽  
Particle Swarm ◽  
Economic Cost ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Active Distribution Network

This work aims at solving complex problems of the optimal scheduling model of active distribution network, teaching strategies are proposed to improve the global search ability of particle swarm optimization. Moreover, based on the improved Euclidean distance cyclic crowding sorting strategy, the convergence ability of Li Zhiquan algorithm is improved. With the cost and voltage indexes of the energy storage system of the distribution network as the goal, different optimized configuration schemes are constructed, and the improved HTL-MOPSO algorithm is adopted to find the solution. The results show that compared with the traditional TV-MOPSO algorithm, the proposed algorithm has better convergence performance and optimization ability, and has a lower economic cost. In short, the algorithm proposed can provide a basis for improving the optimization of active distribution network scheduling strategies.

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