Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D pa...
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Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
A co-evolutionary algorithm based ordinary differential equations of multi input multi output continue chaos system identification is proposed. Structures of ODEs are optimized using GP (Genetic Programming), and corr...
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ISBN:
(纸本)9789881563811
A co-evolutionary algorithm based ordinary differential equations of multi input multi output continue chaos system identification is proposed. Structures of ODEs are optimized using GP (Genetic Programming), and corresponding parameters of ODEs are tuning via GA. Fitness degrees of these individuals which represent suitable structure of ODEs are high than others, because their parameters are optimized by GA. Both structures and corresponding parameters will be evolved using the proposed coevolution GP-GA method. Population initialization, genetic operator, fitness calculation and evolution scheme of multi-population GP are given. comparisons are made among the proposed method, RBF neural network based method and fuzzy clustering based method, simulation results show the efficiency of the method.
In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in ne...
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In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.
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