An improved particle swarm optimization with clone selection principle for dynamic economic emission dispatch

Qian, Shuqu; Wu, Huiqiang; Xu, Guofeng

doi:10.1007/s00500-020-04861-4

Cited by 35 publications

(16 citation statements)

References 39 publications

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“…The data of Cases I-II are derived from (Basu, 2008;Mohammadi-ivatloo et al, 2012;Qian et al, 2020), including predicted power demand (PD), unit information, and coefficients in transmission loss. Fuel costs and constraint violations are counted in Table 9 and are compared with the current popular literature, including the new enhanced harmony search (NEHS), the artificial immune system (AIS), the hybrid DE and sequential quadratic programming (DE-SQP), the hybrid PSO and sequential quadratic programming (PSO-SQP), the efficient fitness-based differential evolution algorithm (EFDE), the hybrid seeker optimization algorithm (SOA) and sequential quadratic programming method (SOA-SQP), the simulated annealing (SA), a hybrid genetic algorithm and bacterial foraging approach (HCRO), and the improved bacterial foraging algorithm (IBFA).…”

Section: Scenario A: Only Units Without Plug-in Electric Vehiclesmentioning

confidence: 99%

A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

Yang

Peng²,

Feng

et al. 2021

Front. Energy Res.

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Massive popularity of plug-in electric vehicles (PEVs) may bring considerable opportunities and challenges to the power grid. The scenario is highly dependent on whether PEVs can be effectively managed. Dynamic economic dispatch with PEVs (DED with PEVs) determines the optimal level of online units and PEVs, to minimize the fuel cost and grid fluctuations. Considering valve-point effects and transmission losses is a complex constrained optimization problem with non-smooth, non-linear, and non-convex characteristics. High efficient DED method provides a powerful tool in both power system scheduling and PEVs charging coordination. In this study, firstly, PEVs are integrated into the DED problem, which can carry out orderly charge and discharge management to improve the quality of the grid. To tackle this, a novel real-coded genetic algorithm (RCGA), namely, dimension-by-dimension mutation based on feature intervals (GADMFI), is proposed to enhance the exploitation and exploration of conventional RCGAs. Thirdly, a simple and efficient constraint handling method is proposed for an infeasible solution for DED. Finally, the proposed method is compared with the current literature on six cases with three scenarios, including only thermal units, units with disorderly PEVs, and units with orderly PEVs. The proposed GADMFI shows outstanding advantages on solving the DED with/without PEVs problem, obtaining the effect of cutting peaks and filling valleys on the DED with orderly PEVs problem.

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Section: Scenario A: Only Units Without Plug-in Electric Vehiclesmentioning

confidence: 99%

A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

Yang

Peng²,

Feng

et al. 2021

Front. Energy Res.

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“…The main operation mode of the scheduling framework can be divided into two parts, which are the uplink of demand side target and supply side target response state and the dispatch instruction of the scheduling center [5,6]. The specific processes of the two operation modes are as follows:…”

Section: Establishing the Framework Of Art Design Resource Scheduling Optimization In Colleges And Universitiesmentioning

confidence: 99%

Optimization Method of Art Design Resource Scheduling for 6G Network Environment in Colleges and Universities

Meng

Yang

2021

Wireless Communications and Mobile Computing

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In order to solve the problem of low flexibility margin of traditional art and design resource scheduling in colleges and universities, an optimization method for art and design resource scheduling in the 6G network environment has been designed. By determining the flexibility margin index of university art and design resource scheduling, the scheduling optimization model is established, the scheduling communication parameters are set for the 6G network environment, the delay of university art and design resource scheduling is perceived, the period of insufficient flexibility is searched, and elimination measures are taken to realize the optimization of university art and design resource scheduling. The experimental results show that the margin of the designed scheduling method is always higher than that of the experimental control group in the same scheduling period, which can solve the problem of low scheduling flexibility margin of traditional methods.

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“…So, there is always a chance of improvement in the performance of the algorithm. This provoked the researchers to enhance the original optimization algorithms with different initial design techniques (Wunnava et al 2020b), remodeling/modifying the search patterns (Qian et al 2020;Wunnava et al 2020a, c;Guha et al 2020) or hybridizing the optimization algorithms (Zhu et al 2020). In this context, opposition based learning (OBL) (Tizhoosh 2005) is a method used to enrich the exploration of any optimization algorithm, because it utilizes the information of opposite relationships among entities to select the best one.…”

Section: Introductionmentioning

confidence: 99%

Adaptive opposition slime mould algorithm

2021

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Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, the SMA uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, we investigate an adaptive approach to decide whether opposition based learning (OBL) will be used or not. Sometimes the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The qualitative and quantitative analysis of AOSMA is reported using 29 test functions consisting of 23 classical test functions and 6 recently used composition functions from the IEEE CEC 2014 test suite. The results are compared with state-of-the-art optimization methods. Results presented in this paper show that AOSMA's performance is better than other optimization algorithms. The AOSMA is evaluated using Wilcoxon's rank-sum test. It also ranked one in Friedman's mean rank test. The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems.

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An improved particle swarm optimization with clone selection principle for dynamic economic emission dispatch

Cited by 35 publications

References 39 publications

A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

A Novel Real-Coded Genetic Algorithm for Dynamic Economic Dispatch Integrating Plug-In Electric Vehicles

Optimization Method of Art Design Resource Scheduling for 6G Network Environment in Colleges and Universities

Adaptive opposition slime mould algorithm

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