Study on Optimization of Urban Rail Train Operation Control Curve Based on Improved Multi-Objective Genetic Algorithm A multi-objective improved genetic algorithm is constructed to solve the train operation simulation model of urban rail train and find the optimal operatio... X Wang,Q Wang - 物联...
In this chapter, optimal gene subset is selected and sample clustering is performed simultaneously using Multi-Objective Genetic Algorithm (MOGA). Different versions of MOGA are employed to choose the optimal gene subset, where natural number of optimal clusters of samples is automatically obtained at...
遗传算法(genetic algorithm) Multi-objective shape optimization of a plate-fin heat exchanger using CFD and multi-objective genetic algorithm 多目标遗传算法优化 Multi-objective Mobile Robot Path Planning Based onImproved Genetic Algorithm 多目标遗传算法优化策略研究 遗传算法(Genetic Algorithm GA) Multi-obje...
Study on multi-objective optimization of flow allocation in a multi-commodity stochastic-flow network with unreliable nodes. Journal of Applied Mathematics and Computing, 28 (1-2), pp. 185-198. :Q. Liu, Q. Zhao and W. Zang, Study on multi-objective optimization of flow allocation in a ...
Combining the merits of variable structure control and fuzzy logic, a fuzzy variable structure control based on multi-objective competitive genetic algorithms was developed.A two-dimension fuzzy tuning mechanism was designed for bound-ary width tuning due to the sliding mode plane function and its cha...
We investigate the optimization performance of multi-objective chaotic evolution (MOCE) algorithm with implementations using different chaotic systems. A comparison experiment of MOCE algorithms with four chaotic systems are employed in MOCE to analyse whether chaotic systems will affect the optimization perf...
4. Multiobjective particle swarm optimization based on crowding distance sorting MOPSO algorithm should guarantee particle swarm evolve to the optimal Pareto optimal front, and maintain the convergence and diversity of Pareto optimal solution also, which are different from single PSO algorithm just for ...
This work demonstrates a case-study to create machine-learning based process models from process data, which are connected into an overall process flowsheet and provide a high level of numerical stability for further multi-objective optimization. The used models are black-box and gray-box models, ...
In a previous work the authors proposed using a multi-objective genetic algorithm – based method, NSGA – II, to optimize two clustering validity measures simultaneously. In this paper we use another multi-objective optimizer, NSPSO, which is based on the particle swarm optimization algorithm, ...
2022) is a modern optimisation algorithm developed recently for solving complex and multi-objective problems. It works based on horse behaviour, like hierarchy, sociability, grazing, imitation, roaming, and defence. MiarNaeimi et al. (2021) suggested an HHO technique to provide suitable solutions ...