A Risk-Based Multiobjective Optimization Framework to Enhance the Safety of Horizontal Curves with Limited Sight Distance

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

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A Data-Driven Multiobjective Optimization Framework for Hyperdense 5G Network Planning

IEEE Access ◽

10.1109/access.2020.3023452 ◽

2020 ◽

Vol 8 ◽

pp. 169423-169443

Author(s):

Beneyam Berehanu Haile ◽

Edward Mutafungwa ◽

Jyri Hamalainen

Keyword(s):

Multiobjective Optimization ◽

Network Planning ◽

Data Driven ◽

Optimization Framework ◽

5G Network

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Adaptive robust optimization with minimax regret criterion: Multiobjective optimization framework and computational algorithm for planning and scheduling under uncertainty

Computers & Chemical Engineering ◽

10.1016/j.compchemeng.2017.09.026 ◽

2018 ◽

Vol 108 ◽

pp. 425-447 ◽

Cited By ~ 18

Author(s):

Chao Ning ◽

Fengqi You

Keyword(s):

Multiobjective Optimization ◽

Robust Optimization ◽

Computational Algorithm ◽

Minimax Regret ◽

Planning And Scheduling ◽

Optimization Framework ◽

Scheduling Under Uncertainty ◽

Minimax Regret Criterion

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Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

Mathematical Problems in Engineering ◽

10.1155/2020/7695419 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Hong Zhang ◽

Guangchen Bai ◽

Lukai Song

Keyword(s):

Genetic Algorithm ◽

Multiobjective Optimization ◽

Design Optimization ◽

Optimal Algorithm ◽

Multicomponent System ◽

Kriging Model ◽

Surface Model ◽

Optimization Framework ◽

Comparison Results ◽

Multiobjective Design

To improve the accuracy and efficiency of multiobjective design optimization for a multicomponent system with complex nonuniform loads, an efficient surrogate model (the decomposed collaborative optimized Kriging model, DCOKM) and an accurate optimal algorithm (the dynamic multiobjective genetic algorithm, DMOGA) are presented in this study. Furthermore, by combining DCOKM and DMOGA, the corresponding multiobjective design optimization framework for the multicomponent system is developed. The multiobjective optimization design of the carrier roller system is considered as a study case to verify the developed approach with respect to multidirectional nonuniform loads. We find that the total standard deviation of three carrier rollers is reduced by 92%, where the loading distribution is more uniform after optimization. This study then compares surrogate models (response surface model, Kriging model, OKM, and DCOKM) and optimal algorithms (neighbourhood cultivation genetic algorithm, nondominated sorting genetic algorithm, archive microgenetic algorithm, and DMOGA). The comparison results demonstrate that the proposed multiobjective design optimization framework is demonstrated to hold advantages in efficiency and accuracy for multiobjective optimization.

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