A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization

Zhang, Yongfeng; Chiang, Hsiao-Dong

doi:10.1109/tcyb.2016.2577587

Cited by 41 publications

(15 citation statements)

References 49 publications

(34 reference statements)

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“…Next, results indicate that the proposed approach has an average best objective value (ABO) of 7.58 min considering 25 independent runs [45], which is significantly lower than the requirements established by the Brazilian ISO. Then, the proposed approach scalability for both power system planning and short-term operation is verified, as its computational performance does not present a critical limiting factor towards its application considering practical ISO standards requirements.…”

Section: A Proposed Intelligent Rmps Allocationmentioning

confidence: 91%

Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

et al. 2020

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New opportunities enabled by renewable mobile power stations (RMPSs), in association with microgrids (MGs) ability to operate islanded to the main grid, present a likely solution to ensure power system novel reliability requirements in face of the increasing number of severe disruptive events. In this perspective, this paper proposes a novel intelligent RMPS allocation method to support MGs during scheduled islanded operation. In this mode, the isolated operation of a MG is beforehand planned to mitigate the effects caused by possible predictable contingencies, e.g. weather-related events, as well as programmed maintenance. For this, first a novel energy management strategy (EMS) based on dynamic programming specifically designed for MG scheduled islanded operation is proposed. Next, a particle swarm optimization (PSO) method modified to ensure a continuous search space in the face of discrete load shedding policies is developed to determine the adequate allocation of RMPS. The proposed method is simulated considering different scenarios for a MG system considering local distributed generation capacity, electric vehicles penetration, load shedding based on priority groups, as well as technical and operational limits. The obtained results showcase the proposed approach ability to meaningfully improve MG service capacity during scheduled islanded operation.

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Section: A Proposed Intelligent Rmps Allocationmentioning

confidence: 91%

Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

et al. 2020

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“…PSO was employed to obtain the initial clustering center of the K-means algorithm rather than randomly initializing the K-means algorithm [33]. Compared with the random initialization K-means algorithm, it was more efficient for the PSO-based K-means algorithm to search for the near-global solution or global optimal solution and enhance the clustering accuracy and computational efficiency [34,35].…”

Section: Initial Parameter Calculationmentioning

confidence: 99%

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

Shao

Chen

2019

Energies

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The electric arc furnace (EAF) contributes to almost one-third of the global iron and steel industry, and its harmonic pollution has drawn attention. An accurate EAF harmonic model is essential to evaluate the harmonic pollution of EAF. In this paper, a data-driven compartmental modeling method (DCMM) is proposed for the multi-mode EAF harmonic model. The proposed DCMM considers the coupling relationship among different frequencies of harmonics to enhance the modeling accuracy, meanwhile, the dimensions of the harmonic dataset are reduced to improve computational efficiency. Furthermore, the proposed DCMM is applicable to establish a multi-mode EAF harmonic model by dividing the multi-mode EAF harmonic dataset into several clusters corresponding to the different modes of the EAF smelting process. The performance evaluation results show that the proposed DCMM is adaptive in terms of establishing the multi-mode model, even if the data volumes, number of clusters, and sample distribution change significantly. Finally, a case study of EAF harmonic data is conducted to establish a multi-mode EAF harmonic model, showing that the proposed DCMM is effective and accurate in EAF modeling.

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“…Inspired by nature, a large variety of metaheuristic algorithms [1] have been proposed that provide optimal or near-optimal solutions to various complex large-scale problems that are difficult to solve using traditional techniques. Some of the many successful metaheuristic approaches include particle swarm optimization (PSO) [2,3], cooperative coevolution [4][5][6], seagull optimization algorithm [7], GRASP [8], clustering algorithm [9], and differential evolution (DE) [10,11], among others.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

et al. 2019

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Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO.

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A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization

Cited by 41 publications

References 49 publications

Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

Intelligent RMPS Allocation for Microgrids Support During Scheduled Islanded Operation

Data-Driven Compartmental Modeling Method for Harmonic Analysis—A Study of the Electric Arc Furnace

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

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