Linear Population Size Reduction Research Articles

Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F, which controls the amplitude of a perturbation step on the current solutions and the crossover rate Cr, which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and Cr values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems.

Horse herd optimization algorithm for economic dispatch problems

This article applies the horse herd optimization (HHO) algorithm to convoluted economic dispatch (ED) problems. HHO mimics the social behaviour of horses of different ages using six significant traits: grazing, hierarchy, sociability, imitation, defence mechanism and roam. The efficacy of the HHO method is demonstrated on five different ED problems, namely, valve-point effects, prohibited feasible area, ramp rate limits and multiple fuels. The simulated outcomes of the recommended method are comparable to those obtained by established artificial intelligence methods. Comparative and statistical analyses demonstrate that the proposed HHO algorithm performs well and can produce superior results to some other well-known and established algorithms, namely, differential evolution (DE), success-history based adaptive differential evolution with linear population size reduction (L-SHADE) and covariance matrix adaptation–evolution strategy (CMA-ES).

Enhancing the search ability of a hybrid LSHADE for global optimization of interplanetary trajectory design

The global optimization of interplanetary trajectory design is a challenge and tough problem in deep space. To cope with the extreme nonlinearity of the search space and a large number of locally optimal solutions, a novel hybrid algorithm based on success-history based adaptive differential evolution with linear population size reduction (LSHADE), called HLSHADE, is proposed. In HLSHADE, after the global search finds a better solution, a new two-step local search with an adaptive control parameters strategy is designed to enhance the exploitation ability of HLSHADE. The presented method is tested on well-known global trajectory optimization problems (GTOPs) developed by the Advanced Concepts Team of the European Space Agency (ESA). Experimental results have demonstrated the competitive performance of HLSHADE with respect to the three sets of compared algorithms, including LSHADE and ten of its variants, six famous algorithms in the IEEE Competitions on Evolutionary Computation (CEC) and eleven algorithms in the software platform PyGMO developed by the ESA's Advanced Concepts Team.

Effective Transmission Congestion Management via Optimal DG Capacity Using Hybrid Swarm Optimization for Contemporary Power System Operations

Managing transmission congestion had been a major problem with growing competition in the power networks. Competitiveness emerges through the network’s reconfiguration and the proliferation of secondary facilities. Congestion of transmission lines is a critical issue, and their regulation poses a technical challenge as the power system is deregulated. The present research illustrates a multi-objective strategy for reaching the optimal capabilities of distributed generators (DG) like wind power plants and geothermal power-producing plants to alleviate congestion throughout the transmission network. Goals such as congestion management during power delivery, power loss reduction, power flow improvement with the enhancement of voltage profile, and investment expenditure minimization are considered to boost the network’s technological and economic reliability. The congestion management is achieved using the locational marginal price (LMP) and calculation of transmission congestion cost (TCC) for the optimal location of DG. After identification of congested lines, DG is optimally sized by particle swarm optimization (PSO) and a newly proposed technique that combines the features of modified IL-SHADE and PSO called hybrid swarm optimization (HSO) which employs linear population size reduction technique which improves its performance greatly by reducing the population size by elimination of least fit individuals at every generation giving far better results than those obtained with PSO. In addition, optimal rescheduling of generations from generators has been done to fulfill the load demand resulting in alleviation of congested lines thereby enhancing the performance of the network under investigation. The performance of the proposed methodology of HSO and PSO has been tested successfully on standard IEEE-30 & IEEE-57 bus configurations in a MATLAB environment with the application of MATPOWER power system package.

Fitness Distance Balance Based LSHADE Algorithm for Energy Hub Economic Dispatch Problem

This paper presents an improve version of Linear Population Size Reduction Success History Based Adaptive Differential Evolution (LSHADE) algorithm for solving global optimization problems. The aim of this study was diversify the search process and to improve the convergence performance of the LSHADE algorithm. In the proposed algorithm called FDB-LSHADE, Fitness Distance Balance (FDB) selection method was used to redesign the mutation operator of the LSHADE algorithms. In order to test and validate the performance of the proposed FDB-LSHADE algorithms, a comprehensive experimental study was carried out. For this purpose, it was tested on the CEC14 and CEC17 benchmark problems, consisting of different problem types and dimensions. The results of the FDB-LSHADE were compared to the performance of 8 other up-to-date and highly preferred metaheuristic search (MHS) algorithms. In addition, the proposed algorithm was applied to solve single- and multi-objective Energy Hub Economic Dispatch (EHED) problems, which were a non-convex, a nonlinear, and high dimensional problems. In order to evaluate the performance of the proposed algorithm, two non-parametric statistical methods, which are Wilcoxon and Friedman tests, were used. The simulation results of the developed algorithm were compared to previously proposed algorithms available in the literature and the results of the MHS algorithms. The results showed that the FDB-LSHADE was a superior performance compared to other MHS algorithms for solving both benchmark problems and EHED problems.

Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

Recently, the numerical optimization field has attracted the research community to propose and develop various metaheuristic optimization algorithms. This paper presents a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA). The proposed algorithm is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for solving optimization problems. The dynamic search behavior of honey badger with digging and honey finding approaches are formulated into exploration and exploitation phases in HBA. Moreover, with controlled randomization techniques, HBA maintains ample population diversity even towards the end of the search process. To assess the efficiency of HBA, 24 standard benchmark functions, CEC’17 test-suite, and four engineering design problems are solved. The solutions obtained using the HBA have been compared with ten well-known metaheuristic algorithms including Simulated annealing (SA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Success-History based Adaptive Differential Evolution variants with linear population size reduction (L-SHADE), Moth-flame Optimization (MFO), Elephant Herding Optimization (EHO), Whale Optimization Algorithm (WOA), Grasshopper Optimization Algorithm (GOA), Thermal Exchange Optimization (TEO) and Harris hawks optimization (HHO). The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration–exploitation balance, as compared to other methods used in this study. The source code of HBA is currently available for public at https://www.mathworks.com/matlabcentral/fileexchange/98204-honey-badger-algorithm.

Improved ensemble of differential evolution variants.

In the field of Differential Evolution (DE), a number of measures have been used to enhance algorithm. However, most of the measures need revision for fitting ensemble of different combinations of DE operators—ensemble DE algorithm. Meanwhile, although ensemble DE algorithm may show better performance than each of its constituent algorithms, there still exists the possibility of further improvement on performance with the help of revised measures. In this paper, we manage to implement measures into Ensemble of Differential Evolution Variants (EDEV). Firstly, we extend the collecting range of optional external archive of JADE—one of the constituent algorithm in EDEV. Then, we revise and implement the Event-Triggered Impulsive (ETI) control. Finally, Linear Population Size Reduction (LPSR) is used by us. Then, we obtain Improved Ensemble of Differential Evolution Variants (IEDEV). In our experiments, good performers in the CEC competitions on real parameter single objective optimization among population-based metaheuristics, state-of-the-art DE algorithms, or up-to-date DE algorithms are involved. Experiments show that our IEDEV is very competitive.

The Real-Life Application of Differential Evolution with a Distance-Based Mutation-Selection

This paper proposes the real-world application of the Differential Evolution (DE) algorithm using, distance-based mutation-selection, population size adaptation, and an archive for solutions (DEDMNA). This simple framework uses three widely-used mutation types with the application of binomial crossover. For each solution, the most proper position prior to evaluation is selected using the Euclidean distances of three newly generated positions. Moreover, an efficient linear population-size reduction mechanism is employed. Furthermore, an archive of older efficient solutions is used. The DEDMNA algorithm is applied to three real-life engineering problems and 13 constrained problems. Seven well-known state-of-the-art DE algorithms are used to compare the efficiency of DEDMNA. The performance of DEDMNA and other algorithms are comparatively assessed using statistical methods. The results obtained show that DEDMNA is a very comparable optimiser compared to the best performing DE variants. The simple idea of measuring the distance of the mutant solutions increases the performance of DE significantly.

2D multi-area coverage path planning using L-SHADE in simulated ocean survey

Ocean environmental surveys typically involve multi-area coverage path planning tasks. The most important problem is improving the coverage efficiency of the task. A new path planning method based on Successful History-Based Adaptive Differential Evolution variants with Linear population size reduction(L-SHADE) is presented to solve this problem. The method comprises two parts: the part of sub area coverage path planning and the part of finding the optimized sequence of sub area start points. The key idea is establishing the relationship between the starting point of each sub area and the optimized multi-area path. We implement the method through numbering the possible starting point of sub area path and proposing a computing formula. In addition, the results of L-SHADE mutation process are optimized which make L-SHADE possible to apply in multi-area coverage path planning. This method avoids area discretization and exponential growth of computational quantities, and it is suitable for complex areas as well as multi-area. The simulation results with MATLAB showed the improvement of coverage path planning task execution efficiency. Compared with the method thinking of the sub area as the center of it, our method reduced the multi-area coverage path length by 4%–7%. From the simulations and analysis, we concluded that the method is able to improve the efficiency and stability of multi-area coverage path planning.

Effective coordination settings for directional overcurrent relay using hybrid Gradient-based optimizer

This manuscript announces a novel hybrid optimization model comprising the gradient-based optimizer (GBO) and Linear population size reduction technique of Success-History-based Adaptive Differential Evolution (LSHADE) algorithm, so-called GB-LSHADE, for optimal relay coordination problem (ORCP). The ORCP is framed as a nonlinear constrained engineering problem that aims to optimize the time-dial, pickup current, and relay characteristics so that the relay clearance times are minimized. The proposed GB-LSHADE operates in two phases meanwhile the first phase starts with GBO to enrich the exploitative tendencies while the second phase is adopted to boost the local searching competence and avoid the premature convergence. To verify the efficacy of the proposed optimization method, a comprehensive simulation has been conducted on three test-systems (8-bus, 15-bus, and IEEE 30-bus) with different complexities. Many scenarios including fixed and varied relay curves as per IEC 60255 are demonstrated. At a final stage of this work, the computational complexity of the proposed approach is carried out and analyzed. It can be confirmed that the adapted optimal settings produced by the GB-LSHADE achieve the full co-ordination of the protection without any violations for the systems under study. In addition, the numerical simulated results show that the GB-LSHADE supersedes the other viable optimizers reported in specialized literature thru further validations and comprehensive comparisons.

Control architecture of autonomous underwater vehicle for coverage mission in irregular region

This paper proposes a control architecture of autonomous underwater vehicle (AUV) for coverage mission in irregular region (CMIR) with an improved task planner. The CMIR control architecture is structured in four layers: perception layer, reactive layer, decision making layer and execution layer. To improve the task planner in decision making layer of control architecture, we adopted convex partitioning method and Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including linear population size reduction (L-SHADE) as the computing engine, which make CMIR control architecture more suitable to coverage problems. Through statistical analysis, we obtained a set of optimized control parameters. Simulation experiments were carried out, three task planners were compared in terms of convergence speed, optimized path length and computing time consumption. The results show that the coverage efficiency of the task planner based on L-SHADE is higher than that of task planner based on particle swarm optimization (PSO) and differential evolution (DE). Sea trial was performed with Sailfish-AUV, which proved that the CMIR control architecture is feasible in actual marine environment.

Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization

Particle Swarm Optimization (PSO) is a simple yet efficient population based evolutionary algorithm, which has been widely applied to solve global optimization problems. Similar to other evolutionary algorithms (EAs), PSO algorithm still suffers from loss of diversity and slow convergence caused by the contradiction between exploration and exploitation abilities. In order to address the drawbacks, we propose Self-Adaptive two roles hybrid learning strategies-based particle swarm optimization (SAHLPSO) to improve optimization performance by using exploration-role and exploitation-role learning strategies with self-adaptively updating parameters manner. The exploration-role learning strategies with external archive adopt comprehensive learning to generate the learning exemplars, which can effectively maintain population diversity and thus avoid premature. On the other hand, the exploitation-role learning strategies utilize elitism learning to generate the learning exemplars so as to improve convergence performance. Considering that the performance improvement of PSO depends on not only the adaptation of different learning strategies but also the reasonable parameter settings, we also develop a self-adaptive parameter updating manner based on success history information. This manner can automatically update the learning parameters to appropriate values and avoid a user’s prior knowledge about the relationship between the parameter settings and the characteristics of optimization problems. In addition, the usage of the linear population size reduction is also helpful to well balance the exploration and exploitation along with the process of evolution. Simulation results on some classical and CEC2013 benchmark functions show that LSAHLPSO is better than other PSO variants or at least comparable to other state-of-the-art evolutionary algorithms.

A knowledge-based differential covariance matrix adaptation cooperative algorithm

In this paper, a knowledge-based differential covariance matrix adaptation cooperative algorithm (DCMAC) is proposed for continuous problems. On the basis of combining successful history-based adaptive DE variants with linear population size reduction (LSHADE) and covariance matrix adaptation evolutionary strategy (CMA-ES), DCMAC proposes a strategy based on knowledge reward and punishment to achieve the purpose of collaborative optimization. Afterward, an adaptive learning mechanism is introduced to optimize the parameters to balance the exploitation and exploration of DCMAC. This process enables the algorithm to have global search capability. Finally, the niching-based population size reduction mechanism is introduced to improve the local search ability of the DCMAC. A weighted mutation strategy with dynamic greedy p value and covariance matrix adaptation (CMA) sampled based on differential vector are presented. Meanwhile, the knowledge acquired in the previous iteration process is adopted in the algorithm to select a mutation strategy for generating the new candidate solutions in the next iteration. The niching population size reduction mechanism is introduced to maintain the diversity of the population and compared with the other classical population size reduction methods. The optimal combination of parameters in the DCMAC algorithm is testified by the design of the experiment. Furthermore, the DCMAC is testified on the CEC2017 benchmark functions. The effectiveness and efficiency of the DCMAC are demonstrated by the experimental results in solving complex continuous problems.

Evolving fuzzy k-nearest neighbors using an enhanced sine cosine algorithm: Case study of lupus nephritis

Because of its simplicity and effectiveness, fuzzy K-nearest neighbors (FKNN) is widely used in literature. The parameters have an essential impact on the performance of FKNN. Hence, the parameters need to be attuned to suit different problems. Also, choosing more representative features can enhance the performance of FKNN. This research proposes an improved optimization technique based on the sine cosine algorithm (LSCA), which introduces a linear population size reduction mechanism for enhancing the original algorithm's performance. Moreover, we developed an FKNN model based on the LSCA, it simultaneously performs feature selection and parameter optimization. Firstly, the search performance of LSCA is verified on the IEEE CEC2017 benchmark test function compared to the classical and improved algorithms. Secondly, the validity of the LSCA-FKNN model is verified on three medical datasets. Finally, we used the proposed LSCA-FKNN to predict lupus nephritis classes, and the model showed competitive results. The paper will be supported by an online web service for any question at https://aliasgharheidari.com.

Path planning of AUV during diving process based on behavioral decision-making

This study proposes a path planning method based on behavioral decision-making (PPM-BBD). An optimized path is planned for autonomous underwater vehicle (AUV) to save energy during the diving process. The key idea is to apply success-history based adaptive differential evolution algorithm including linear population size reduction (L-SHADE) to the optimization of energy. In addition, motion constraints are considered during the diving process to ensure that the path points are reachable. We take the sequence of motion angles as the population, and propose an angle modification strategy to apply to the population evolution process, so that the angle sequence remains in the space of feasible solutions. The modification strategy lays a theoretical foundation for the application of L-SHADE in this path planning problem. The performance of PPM-BBD is evaluated on data from simulation test. The most suitable control parameters are determined through simulation experiments. The effects of PPM-BBD with L-SHADE and five types of DE are compared. The experimental evaluation was performed through sea trials in Tuandao Bay, Qingdao, China. Results show that PPM-BBD saves at least 9% energy compared to the other algorithms. It is concluded that it is workable to obtain a reachable path with optimized energy consumption during diving process.

An improved gaining-sharing knowledge algorithm for parameter extraction of photovoltaic models

The optimal operation of solar cells depends on the accurate determination of parameters in the Photovoltaic (PV) models, such as resistance and currents, which may vary due to unstable weathers conditions and equipment aging. The precise selection of these parameters resembles a multi-variable, nonlinear and multi-modal problem. Despite a few parameter extraction techniques being available to solve such a problem, more-accurate and advanced solutions still present a challenging research question. This paper therefore proposes an improved gaining-sharing knowledge (IGSK) algorithm to accurately and precisely extract the parameters of PV models. The improvement in the classical GSK algorithm is incorporated by introducing an adaptive mechanism to automatically adjust the value of the knowledge rate parameter. This adaptive mechanism ensures the balance between the number of dimensions updated by the junior gaining-sharing phase and the number of dimensions updated by the senior gaining-sharing phase. A bound-constraint handling method is also presented and a linear population size reduction technique is used to boost the speed of convergence and to maintain a tared-off between the exploration and exploitation properties. The efficacy of the proposed IGSK has been demonstrated by considering three different PV modules models, i.e., single diode, double diode, and PV modules and two other commercial ones (Thin Film ST40 and Mono-crystalline SM55). For those modules, the proposed IGSK receptively produces the following outcomes: 0.00098602188, 0.0009827277, 0.0024250749, 0.0017298137, and 0.016600603. The statistical obtained results demonstrate that the IGSK indicates competitive or even better performance on convergence speed, accuracy and reliability compared with other competing techniques. Therefore, the proposed approach is believed to be an effective and efficient alternative for parameter extraction of PV models.

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is applied to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

A hybridized multi-algorithm strategy for engineering optimization problems

This paper presents a new multi-hybrid algorithm (MHA) based on the hybridization concepts incorporated in the naked mole-rat algorithm (NMRA). The new algorithm uses the exploratory concepts of whale optimization algorithm (WOA), moth flame optimization (MFO) and marine predator algorithm (MPA) to enhance the worker phase or the exploration operation in NMRA. In order to overcome the problem of local optima stagnation, a new stagnation phase inspired from the concepts of GWO and cuckoo search (CS) algorithm is also added. Apart from these modifications, self-adaptivity has been incorporated by adapting all the parameters of the proposed algorithm. Here five new mutation strategies including simulated annealing (sa), exponentially decreasing (exp), chaos based (chaotic), linearly decreasing (linear) and oscillatory inertia weight (oscillating) have been exploited for different parameters of all the algorithms under study so that no user based parametric adjustments are required. For performance evaluation, the MHA is subjected to CEC 2005, CEC 2014 benchmark problems and image thresholding problem. The comparison on variable population sizes and dimension size has been done to prove the significance of the proposed algorithm over variable function evaluations and higher dimensional problems. From the experimental results, it can be seen that proposed MHA performs better than adaptive differential evolution with optional external archive (JADE), self adaptive differential evolution (SaDE), opposition and exponential whale optimization algorithm (OEWOA), sine cosine crow search algorithm (SCCSA), fractional-order calculus-based flower pollination algorithm (FA-FPO), success-history based parameter adaption differential evolution (SHADE), SHADE with linear population size reduction hybrid with semi-parameter adaptation of CMA-ES (LSHADE-SPACMA), Laplacian biogeography-based optimization (LX-BBO), random walk grey wolf optimizer (RW-GWO), improved symbiotic organisms search (ISOS), variable neighbourhood bat algorithm (VNBA), improved elephant herding optimization (IMEHO) and others. Statistical results in terms of Wilcoxon’s rank-sum test, Freidman’s test and convergence profiles further validate the superior performance of the proposed MHA algorithm. The source code can be downloaded from https://github.com/rohitsalgotra.

Planning and Research of Distribution Feeder Automation with Decentralized Power Supply

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches’ operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration.