A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

Tian, Ye; Zhang, Tao; Xiao, Jianhua; Zhang, Xingyi; Jin, Yaochu

doi:10.1109/tevc.2020.3004012

Cited by 388 publications

(117 citation statements)

References 54 publications

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“…Figure 4(c) shows that Pareto fronts obtained by 10 algorithms with 10 runs are converted into an approximate ideal Pareto surface. To better compare the convergence and diversity of the algorithms, the performance indexes of four algorithms are evaluated in Table 5, including inverted generational distance (IGD) [30], generational distance (GD) [31], pure diversity (PD) [32], hyper volume (HV) [33], DM (diversity metric) [34], spread [35], and spacing [36], where IGD [37] and HV denote the accuracy and diversity of algorithm; GD considers the accuracy of the algorithm; PD and spread denote the diversity of the algorithm; spacing denotes the evenness of the solutions obtained by algorithm; GD, IGD, spread, and spacing are the negative indexes, while DM, HV, and PD are the positive indexes [38][39][40].…”

Section: Static Test Experimentsmentioning

confidence: 99%

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

et al. 2021

Mathematical Problems in Engineering

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Large-scale renewable energy sources connected to the grid bring new problems and challenges to the automatic generation control (AGC) of the power system. In order to improve the dynamic response performance of AGC, a biobjective of complementary control (BOCC) with high-participation of energy storage resources (ESRs) is established, with the minimization of total power deviation and the minimization of regulation mileage payment. To address this problem, the strength Pareto evolutionary algorithm is employed to quickly acquire a high-quality Pareto front for BOCC. Based on the entropy weight method (EWM), grey target decision-making theory is designed to choose a compromise dispatch scheme that takes both of the operating economy and power quality into account. At last, an extended two-area load frequency control (LFC) model with seven AGC units is taken to verify the effectiveness and the performance of the proposed method.

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Section: Static Test Experimentsmentioning

confidence: 99%

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

et al. 2021

Mathematical Problems in Engineering

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“…Reducing the overlap between sensing areas of adjacent UAVs is beneficial to improve the efficiency of marine coverage. Using the segmentation methods in references (Papatheodorou et al, 2016;Tian et al, 2020b), a sensing set segmentation method that can avoid overlapping sensing sets between adjacent UAVs is given below.…”

Section: Undirected Sensing Set Segmentationmentioning

confidence: 99%

Autonomous Control Method of Rotor UAVs for Power Inspection With Renewable Energy Based on Swarm Intelligence

Yao

Jun-hua

Guo³

et al. 2021

Front. Energy Res.

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With the rapid development of renewable energy, the scale of China’s power grid with renewable energy has become much bigger than ever; as a result, we are facing severe challenges in the inspection and maintenance work of power grids that use renewable energy. Focusing on the shortcomings of the traditional manual inspection methods, this paper studies and proposes an optimization algorithm of automatic inspection of Unmanned Aerial Vehicles (UAVs) to improve the efficiency and cost of the inspection and maintenance work of renewable energy power grids. Firstly, the communication network of the swarm intelligence system has been established to transmit the local information sensed by each UAV in real time. Secondly, according to the sensing ability of UAVs, the segmentation model of UAVs overlapping sensing areas is established, which effectively reduces the probability of overlapping UAVs sensing areas. Thirdly, according to the difference between the coverage value and the effective coverage index of each point in the sensing area, the optimization function of the coverage index is given, which makes the UAV give priority to the inspection area with the lower coverage value. Finally, when a UAV completes a local coverage task, the traction speed is introduced to prevent the UAV from stopping, which ensures that the inspection task of the whole area can be completed in a limited time. The numerical simulation results show that the algorithm can effectively control the UAVs to complete the inspection task in the specified area, and compared with the single UAV inspection method, this algorithm can greatly improve the inspection efficiency and reduce the inspection cost.

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“…Due to its simple definition and light implementation, CDP is widely used in various CMOEAs, such as NSGA-II [21] and C-MOEA/D [24]. Nevertheless, CDP prefers feasible solutions over infeasible ones [25], [26], which always results in premature convergence, especially when the feasible regions are discontinuous or faulted.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Experimental results showed that the C-TAEA can handle CMOPs and CMaOPs with intersected and discontinuous feasible regions [10]. However, as pointed out in [26], its mating selection strategy usually chooses one parent individual from CA and the other from DA. The generated offspring will locate in the middle region between CA and DA; thus, neither possessing better convergence, feasibility than individuals in CA, nor being able to promote the diversity of DA.…”

Section: Background and Related Workmentioning

confidence: 99%

A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

Ming¹

2021

Preprint

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<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

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A Coevolutionary Framework for Constrained Multiobjective Optimization Problems

Cited by 388 publications

References 54 publications

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

Biobjective Optimization-Based Frequency Regulation of Power Grids with High-Participated Renewable Energy and Energy Storage Systems

Autonomous Control Method of Rotor UAVs for Power Inspection With Renewable Energy Based on Swarm Intelligence

A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

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