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Assessment of optimization algorithms capability in distribution network planning: Review, comparison and modification techniques

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2016年 66卷 415-434页

作者： Sedghi, Mandi Ahmadian, Ali Aliakbar-Golkar, Masoud KN Toosi Univ Technol Fac Elect Engn POB 16315-1355 Tehran Iran

Optimal expansion of medium-voltage power networks because of load growth is a combinatorial problem which is important from technical and economic points of view. The planning solutions consist of installation and/or reinforcement of high voltage/medium voltage (HV/MV) substations, feeder sections, distributed generation (DG) and storage units to expand the capacity of the network. The cost objective function of the system should be minimized subject to the technical constraints. Due to the complicacy and the complexity of the problem, it should be solved by modern optimization algorithms. In this paper, the most famous optimization algorithms for solving the distribution network planning problem are reviewed and compared, and some points are proposed to improve the performance of the algorithms. In order to compare the algorithms in practice, and verify the proposed improvement points, the numerical studies on three test distribution networks are presented. The results show that every algorithm has its own advantages and disadvantages in specific conditions. However, in general manner, the hybrid Tabu search/genetic algorithm (TS/GA) and the improved particle swarm optimization (PSO) algorithm proposed in this paper are the best choices for optimal distribution network planning. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distribution network planning Distributed generation Storage optimization algorithms

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Reactive Power Management in Renewable Rich Power Grids: A Review of Grid-Codes, Renewable Generators, Support Devices, Control Strategies and optimization algorithms

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IEEE ACCESS 2018年 6卷 41458-41489页

作者： Sarkar, Mohammad Nazmul Islam Meegahapola, Lasantha Gunaruwan Datta, Manoj RMIT Univ Sch Engn Melbourne Vic 3000 Australia

Power electronic converter (PEC)-interfaced renewable energy generators (REGs) are increasingly being integrated to the power grid. With the high renewable power penetration levels, one of the key power system parameters, namely reactive power, is affected, provoking steady-state voltage and dynamic/transient stability issues. Therefore, it is imperative to maintain and manage adequate reactive power reserve to ensure a stable and reliable power grid. This paper presents a comprehensive literature review on the reactive power management in renewable rich power grids. Reactive power requirements stipulated in different grid codes for REGs are summarized to assess their adequacy for future network requirements. The PEC-interfaced REGs are discussed with a special emphasis on their reactive power compensation capability and control schemes. Along with REGs, conventional reactive power support devices (e.g., capacitor banks) and PEC-interfaced reactive power support devices (e.g., static synchronous compensators) play an indispensable role in the reactive power management of renewable rich power grids, and thus their reactive power control capabilities and limitations are thoroughly reviewed in this paper. Then, various reactive power control strategies are reviewed with a special emphasis on their advantages/disadvantages. Reactive power coordination between support devices and their optimal capacity are vital for an efficient and stable management of the power grid. Accordingly, the prominent reactive power coordination and optimization algorithms are critically examined and discussed in this paper. Finally, the key issues pertinent to the reactive power management in renewable rich power grids are enlisted with some important technical recommendations for the power industry, policymakers, and academic researchers.

关键词： Control strategies grid codes optimization algorithms renewable energy generators (REGs) reactive power solar photovoltaic (PV) wind generation

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A hybrid strategy on combining different optimization algorithms for hazardous gas source term estimation in field cases

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2020年 138卷 27-38页

作者： Wang, Yiduo Chen, Bin Zhu, Zhengqiu Wang, Rongxiao Chen, Feiran Zhao, Yong Zhang, Laobing Natl Univ Def Technol Coll Syst Engn 109 Deya Rd Changsha 410073 Peoples R China Delft Univ Technol Fac Technol Policy & Management Safety & Secur Sci Grp S3G NL-2628 BX Delft Netherlands Acad Mil Sci PLA China Inst War Beijing 100091 Peoples R China

Estimating gas source terms is essential and significant for managing a gas emission accident. optimization method, as a kind of estimation methods, is helpful to figure out the source terms by solving the inverse problem. Significantly, the performance of optimization method on source term estimation is affected by the accuracy of forward dispersion model. To enhance the estimation accuracy, previous works have demonstrated the feasibility of using Back Propagation Neural Network (BPNN) trained by actual experimental datasets as a forward dispersion model. However, the overall accuracy of source estimation is still limited by backward estimation methods. Most related studies used a single optimization algorithm to estimate source terms, which usually fails to realize the requirements of both high calculation accuracy and satisfying computational efficiency. Therefore, a hybrid strategy was proposed in this study to combine optimization algorithms with different characteristics, including particle swarm optimization, genetic algorithm and simulated annealing algorithm, to not only achieve high accuracy in global searching, but also converge to a stable result efficiently. Finally, extensive experiments are conducted to testify our proposed hybrid optimization algorithms. The Skill scores of hybrid optimization algorithms decrease obviously compared to those of single optimization algorithm. Hence, the proposed hybrid strategy is potentially useful for guiding the combination of optimization algorithms for gas source terms estimation, which further contributes to deal with a gas emission accident with satisfying calculation accuracy and computational efficiency. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Gas emission Source term estimation Neural network Hybrid strategy optimization algorithms

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Reverse engineering from spectrophotometric measurements: performances and efficiency of different optimization algorithms

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APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING 2012年第4期108卷 877-889页

作者： Gao, Lihong Lemarchand, Fabien Lequime, Michel Inst Fresnel UMR CNRS 6133 F-13397 Marseille 20 France

A large number of parameters is often required to describe optical dispersion laws, and it is only through the use of an appropriate global optimization procedure that an accurate thin-film index determination can be achieved. In this paper, we propose to investigate the respective performances of three different optimization algorithms, namely Simulated Annealing, Genetic Algorithm and Clustering Global optimization and compare results with a commercial software dedicated to thin-film index determination. This study is restricted to the single-layer thin-film index determination of transparent and absorbing materials. It includes the theoretical study of simulated reflection and transmission spectra, and the experimental characterization of Ta2O5 and Si layers.

关键词： Hyalin Optical dispersion Reverse engineering Commercial software TRANSMISSION SPECTROSCOPY Theoretical Study optimization algorithms Global optimization

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Predicting heat transfer rate of a ribbed triple-tube heat exchanger working with nano fluid using neural network enhanced by advanced optimization algorithms

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POWDER TECHNOLOGY 2021年 381卷 459-476页

作者： Bahiraei, Mehdi Foong, Loke Kok Hosseini, Siavash Mazaheri, Nima Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Islamic Azad Univ Kermanshah Branch Young Researchers & Elite Club Kermanshah Iran

Eight optimizer methods are combined with a perceptron neural network to achieve an optimal network and minimize errors for predicting the heat transfer rate of a ribbed triple-tube heat exchanger operating with the graphene nanoplatelets-based nanofluid. The optimization techniques consist of Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO), Whale optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Colony optimization (ACO), Ant Lion Optimizer (ALO), Biogeography-Based optimization (BBO), and Dragonfly Algorithm (DA). The required data are provided with the aid of numerical simulations. The structural parameters are considered using different rib pitches and heights. The ALO algorithm is the best method for estimating the output. The best performance of this algorithm is gained by population size of 350. By this method, the heat transfer rate is estimated with the Root Mean Square Error (RMSE) values of about 0.0310 and 0.0385 for the training and testing data samples, respectively. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Artificial neural network optimization algorithms Ribbed-triple tube heat exchanger Nanofluid Graphene nanoplatelets

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Gradient-based optimization algorithms for networks of reconfigurable sensors

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CONTROL ENGINEERING PRACTICE 2014年 29卷 74-85页

作者： de Groot, T. H. Krasnov, O. A. Yarovoy, A. G. Delft Univ Technol NL-2628 CD Delft Netherlands

A variety of optimization algorithms has been developed for non-linear and non-convex problems in which numerous reconfigurable sensors need to be assigned to many tasks. The algorithms are based on modified gradient-search methods and inspired by centralized/distributed principles. Numerical evaluation of these algorithms on a statistically large set of optimization problems has shown that while each particular algorithm does not necessarily provide the optimal solution in all possible cases, some are very efficient in solving them. Distributed (agent-based) approaches are usually advocated because of their scalability and speed, but the developed centralized (synchronous) algorithms are shown to be better in terms of speed, and simultaneously in terms of effectiveness, and therefore, in terms of efficiency. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Resource allocation Reconfigurability Distributed sensor networks optimization algorithms Gradient methods

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A sociologically inspired heuristic for optimization algorithms: A case study on ant systems

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EXPERT SYSTEMS WITH APPLICATIONS 2013年第5期40卷 1814-1826页

作者： Ribeiro, Richardson Enembreck, Fabricio Fed Technol Univ Parana UTFPR Dept Informat COINF Res Grp Commun & Informat Technol GETIC Pato Branco Brazil Pontifical Catholic Univ Parana PUCPR Postgrad Program Informat PPGIa Curitiba Parana Brazil

This paper discusses how social network theory can provide optimization algorithms with social heuristics. The foundations of this approach were used in the SAnt-Q (Social Ant-Q) algorithm, which combines theory from different fields to build social structures for state-space search, in terms of the ways that interactions between states occur and reinforcements are generated. Social measures are therefore used as a heuristic to guide exploration and approximation processes. Trial and error optimization techniques are based on reinforcements and are often used to improve behavior and coordination between individuals in a multi-agent system, although without guarantees of convergence in the short term. Experiments show that identifying different social behavior within the social structure that incorporates patterns of occurrence between states explored helps to improve ant coordination and optimization process within Ant-Q and SAnt-Q giving better results that are statistically significant. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Ant systems Collective behavior Social structure Social network theory optimization algorithms

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Magnetic-inspired optimization algorithms: Operators and structures

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 19卷 82-101页

作者： Tayarani-N, M. -H. Akbarzadeh-T, M. -R. Univ Southampton Sch Elect & Comp Sci Southampton SO7 1BJ Hants England Ferdowsi Univ Mashhad Ctr Excellence Soft Comp & Intelligent Informat P Dept Elect Engn Mashhad Iran

A novel optimization algorithm, called the Magnetic optimization algorithms (MOAs), is proposed in this paper which is inspired by the principles of magnetic field theory. In MOA, the possible solutions are some magnetic particles scattered in the search space. In this respect, each magnetic particle has a measure of mass and magnetic field according to its fitness. In this scheme, the fitter magnetic particles are more massive, with stronger magnetic field. In terms of interaction, these particles are located in a structured population and apply a long range force of attraction to their neighbors. Ten different structures are proposed for the algorithm and the structure that offers the best performance is found. Also, to improve the exploration ability of the algorithm, several operators are proposed: a repulsive short-range force, an explosion operator, a combination of short-range force and explosion operator and a crossover interaction between the neighboring particles. In order to test the proposed algorithm and the proposed operators, the algorithm is compared with a variety of existing algorithms on 21 numerical benchmark functions. The experimental results suggest that the proposed algorithm outperforms some of the existing algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： optimization algorithms Genetic algorithms Magnetic optimization algorithms

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Using multiobjective optimization algorithms and decision making support to solve polymer extrusion problems

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POLYMER ENGINEERING AND SCIENCE 2018年第4期58卷 493-502页

作者： Denysiuk, Roman Recio, Gustavo Covas, Jose Antonio Gaspar-Cunha, Antonio Univ Minho Dept Polymer Engn Inst Polymers & Composites Campus Azurem P-4800058 Guimaraes Portugal Univ Carlos III Madrid Dept Comp Sci Ave Univ 30 Madrid 28911 Spain

Single screw extrusion is a major polymer processing operation. Its optimization is crucial for producing good quality products at suitable costs. This study addresses extrusion as a multiobjective optimization problem that can be solved using evolutionary algorithms incorporating decision making and robustness strategies for selecting solutions. This approach enables focusing the search for solutions in favored regions where the preference was defined either by the relative importance of the objectives or determined considering the robustness of solutions against perturbations in the design variables. The outcome of this strategy provides not only a better insight into the problem at hand, but also facilitates the choice of a single solution for practical implementation. The usefulness of the approach is illustrated by several case studies involving the definition of the most adequate operating conditions, of the best screw geometry and the two together. POLYM. ENG. SCI., 58:493-502, 2018. (c) 2017 Society of Plastics Engineers

关键词： optimization algorithms Operating conditions extrusion Solutions decision making support screw extrusion design variable decision making evolutionary algorithm

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Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf optimization algorithms

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IEEE ACCESS 2021年 9卷 77416-77437页

作者： Xu, Zhe Yang, Haichuan Li, Jiayi Zhang, Xingyi Lu, Bo Gao, Shangce Changzhou Inst Technol Sch Comp Informat & Engn Changzhou 213032 Jiangsu Peoples R China Toyama Univ Fac Engn Toyama 9308555 Japan Shanghai Jiao Tong Univ Shanghai Gen Hosp Shanghai 200080 Peoples R China Shanghai Normal Univ Tianhua Coll Fac Engn Shanghai 201815 Peoples R China

As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

关键词： Heuristic algorithms optimization Sociology Licenses Convergence Memetics Chaos Computational intelligence soft computing chaotic local search optimization algorithms grey wolf optimizer meta-heuristics

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