Multi-objective neural network model selection with a graph-based large margin approach

Torres, Luiz C. B.; Castro, Cristiano Leite de; Rocha, Honovan Paz; Almeida, Gilberto Machado de; Braga, Antônio de Pádua

doi:10.1016/j.ins.2022.03.019

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Table 8 shows the proportion and inspection indicators of the three objectives in each of the three planning years calculated by the analytic hierarchy process, which are less than 0.1. As can be seen from Table 8, with the increasing attention paid to ecological protection, the weight of economic objectives will gradually decline, and the weight of ecological objectives will rise accordingly [15,16].…”

Section: Results Analysismentioning

confidence: 99%

Research and Application of Optimal Allocation Model of Water and Soil Resources from the Perspective of Ecological Economics

Dai

Wang

2022

Journal of Sensors

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In order to solve the problem of realizing the rational allocation of water and soil resources in the watershed on the premise of ecological protection under the background of climate change, this paper proposes an optimal allocation model of water and soil resources from the perspective of ecological economics. In this paper, the use area of various land resources is taken as the decision variable, and the total amount of water resources available is taken as the constraint condition, and the multiobjective models under different scenarios are established, respectively. MOGWO algorithm is used to calculate the Pareto solution set under each scenario, and finally AHP is used to select the optimal solution with different decision preferences from Pareto. The experimental results show that under the current situation, the water shortage of the basin is 7.61 × 10 8 m3. In the current planning year (2020), 950.7 km2 of cultivated land should be reduced to meet the water resources situation of the river basin, accounting for about 17.8% of the total cultivated land, which is also in line with the basic policy of “well killing and land shrinking” advocated by the river basin in recent years. In the medium-term planning year (2035), the cultivated land area can be maintained at about 4300. Due to the increase of available water resources, water-saving irrigation area and irrigation water utilization coefficient, the cultivated land area in the long-term planning year (2050) can be increased by about 200 km2 on the basis of the medium-term planning year. Conclusion. The model finally obtains the optimal solution in line with different decision preferences under different scenarios.

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Section: Results Analysismentioning

confidence: 99%

Research and Application of Optimal Allocation Model of Water and Soil Resources from the Perspective of Ecological Economics

Dai

Wang

2022

Journal of Sensors

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Many-Objective Grasshopper Optimization Algorithm (MaOGOA): A New Many-Objective Optimization Technique for Solving Engineering Design Problems

Kalita,

Jangir,

Čep

et al. 2024

Int J Comput Intell Syst

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In metaheuristic multi-objective optimization, the term effectiveness is used to describe the performance of a metaheuristic algorithm in achieving two main goals—converging its solutions towards the Pareto front and ensuring these solutions are well-spread across the front. Achieving these objectives is particularly challenging in optimization problems with more than three objectives, known as many-objective optimization problems. Multi-objective algorithms often fall short in exerting adequate selection pressure towards the Pareto front in these scenarios and difficult to keep solutions evenly distributed, especially in cases with irregular Pareto fronts. In this study, the focus is on overcoming these challenges by developing an innovative and efficient a novel Many-Objective Grasshopper Optimisation Algorithm (MaOGOA). MaOGOA incorporates reference point, niche preserve and information feedback mechanism (IFM) for superior convergence and diversity. A comprehensive array of quality metrics is utilized to characterize the preferred attributes of Pareto Front approximations, focusing on convergence, uniformity and expansiveness diversity in terms of IGD, HV and RT metrics. It acknowledged that MaOGOA algorithm is efficient for many-objective optimization challenges. These findings confirm the approach effectiveness and competitive performance. The MaOGOA efficiency is thoroughly examined on WFG1-WFG9 benchmark problem with 5, 7 and 9 objectives and five real-world (RWMaOP1- RWMaOP5) problem, contrasting it with MaOSCA, MaOPSO, MOEA/DD, NSGA-III, KnEA, RvEA and GrEA algorithms. The findings demonstrate MaOGOA superior performance against these algorithms.

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Improvement accuracy in deep learning: An increasing neurons distance approach with the penalty term of loss function

Wen

Lam

2023

Information Sciences

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Multi-objective neural network model selection with a graph-based large margin approach

Cited by 3 publications

References 33 publications

Research and Application of Optimal Allocation Model of Water and Soil Resources from the Perspective of Ecological Economics

Research and Application of Optimal Allocation Model of Water and Soil Resources from the Perspective of Ecological Economics

Many-Objective Grasshopper Optimization Algorithm (MaOGOA): A New Many-Objective Optimization Technique for Solving Engineering Design Problems

Improvement accuracy in deep learning: An increasing neurons distance approach with the penalty term of loss function

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