Applying graph-based differential grouping for multiobjective large-scale optimization

Cao, Bin; Zhao, Jianwei; Gu, Yu; Ling, Yingbiao; Ma, Xiaoliang

doi:10.1016/j.swevo.2019.100626

Cited by 183 publications

(59 citation statements)

References 22 publications

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“…For future works, we recommend applications of the proposed method to other UAV systems [63][64][65][66][67], adaptive control techniques [68][69][70][71][72], applications of optimization methods such as differential evolution, particle swarm, whale optimizer, differential search, and other optimizers for optimizing parts of the process [73][74][75][76][77][78], distributed optimization [79], and other optimization forms such as robust, memetic, many objective, multiobjective, and fuzzy optimization [80][81][82][83][84][85][86][87].…”

Section: Discussionmentioning

confidence: 99%

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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To improve the predictive ability in trajectory of large unmanned aerial vehicle (UAV) and the calculation performance in complicated circumstances with mixed airspace, multiple aircraft types, and joint operations, the concept of phased trajectory deviation (PTD) is introduced and a corresponding minimal interval algorithm (PTD-MI) is set up. This algorithm is capable of deriving the minimal interval between various aircraft types according to the crosswind impacts and the UAV characteristics at different flight phases and thus achieves the effective safety evaluation in airspace operation. To demonstrate the rationality and generality of the proposed algorithm, several simulation experiments are conducted. Based on the experimental results, flight procedure protection area is plotted by PTD-MI algorithm and compared with that generated by Ground-Based Augmentation System (GBAS). Results indicate that the proposed algorithm is capable of deriving a more scientific basis for airspace assignment and outperform GBAS in dealing with wide-area space problems. And, compared with GBAS, PTD-MI algorithm shows a more stable calculation performance and is easier to output the results. PTD-MI algorithm is proposed under the flight safety regulation designed by the International Civil Aviation Organization (ICAO) and designed to provide effective technical supports for the safe and normal operations of aircrafts.

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Section: Discussionmentioning

confidence: 99%

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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“…Eq. (9) shows that the original GOA parameters are linearly reduced, which dynamically balances exploration and exploitation. However, suppose C is reduced too quickly in the early stage.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Hence, the critical step in dealing with these problems is the solution approach. Optimization is a set of methods that can deal with single-objective tasks [6], multiobjective case studies [7], robust optimization scenarios [8], large-scale optimization tasks with many variables [9], [10],…”

Section: Introductionmentioning

confidence: 99%

Chaotic Arc Adaptive Grasshopper Optimization

et al. 2021

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The grasshopper optimization algorithm (GOA) has become one of the most widely used methods, regardless of its shortcomings in its performance and algorithm design. To further harmonize exploration and exploitation in the GOA, this paper proposed an enhanced variant of the primary method by incorporating chaos theory to address the problems of slow convergence, low solution accuracy, and stagnation. Structural code analysis and previous works reveal that the grasshopper algorithm's internal function is one of these shortcomings' primary sources. The proposed GOA-based method employed an adaptive arc function instead of the grasshopper algorithm's internal function to strengthen and balance the global exploration capabilities and local exploitation capacities without any expensive modifications. Additionally, the chaotic mapping strategy was adopted to update the individual positions of agents iteratively. The individuals near the current optimal solution were disturbed to regenerate the optimal solution and enhance the swarm's diversity. This idea improves the global inspection capacity and discourages falling into local optima. To verify the proposed technique's effectiveness, a comprehensive set of twenty-seven benchmark cases and three engineering design problems were used for validation. We compared the proposed GOA-based method with the WOA, SCA, GOA, DA, CSSA, CGSCA, CLPSO, GL25, and OBWOA. Additionally, we tested the designed GOA against LSHADE, SHADE, EBOwithCMAR, SaDE, MPEDE, and EPSDE. Simulation results demonstrated that the algorithm was substantially superior to the original technique. Its global optimization competence, search accuracy, and convergence performance were notably improved. The results expose the regulation of this internal factor, which significantly affects the quality of the results. Further information about this research and assistance with any request is available at http://aliasgharheidari.com. INDEX TERMSGrasshopper optimization algorithm, swarm intelligence, chaotic maps, engineering design problem.

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“…Artificial neural networks (ANN), as one of the most known AI-based solutions, have received increasing attraction recently [58][59][60][61][62]. More technically, deep learning-based [63][64][65][66], machine learning [67][68][69], decision making-based theories, feature selection-based solutions [70][71][72], extremer machine learning solutions [73][74][75][76], as well as hybrid searching algorithms that enhanced conventional multilayer perceptron like harris hawks optimization [77,78], whale optimizer [79,80], bacterial foraging optimization [81], chaos enhanced grey wolf optimization [82], moth-flame optimizer [74,83], many-objective sizing optimization [84][85][86][87][88][89], Driven Robust Optimization [90], ant colony optimization [91], and global numerical optimization [92]. These techniques are successfully employed in different aspects such as building design [93][94][95][96][97][98][99]…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Applying graph-based differential grouping for multiobjective large-scale optimization

Cited by 183 publications

References 22 publications

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

An Operational Safety Evaluation Method for Manned Transport Aircraft and Large UAV in Mixed Airspace

Chaotic Arc Adaptive Grasshopper Optimization

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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