In order to effectively reduce the cost and completion time of resource scheduling, an optimal scheduling method for marine transportation resources based on wolfswarm optimization is proposed. In order to expand the...
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In order to effectively reduce the cost and completion time of resource scheduling, an optimal scheduling method for marine transportation resources based on wolfswarm optimization is proposed. In order to expand the theoretical system and practical application of wolfswarm optimization, aiming at a series of shortcomings existing in wolf swarm algorithm, the wolfswarm method is optimized in the following two aspects: the first is to improve the internal operation mechanism to develop the performance of wolf swarm algorithm in solving single-objective optimization, multi-peak optimization and multi-objective optimization problems;the second is to introduce other mechanisms, which are integrated into the optimization strategy of the wolf swarm algorithm, so that the algorithm has the ability to deal with multi-peak and multi-objective optimization problems. The optimal scheduling of maritime transportation resource is abstracted into the Travelling Salesman Problem (TSP) problem. The improved wolf swarm algorithm is used to solve the TSP model, and the optimal solution of the TSP model is obtained to realize the optimal scheduling of maritime transportation resource. The simulation results show that the proposed method can effectively reduce resource scheduling costs and task completion time.
Aiming at the problem of unstable load rate in the emergency dispatching process of the EHV transmission network, an automatic emergency dispatching method for the EHV transmission network based on a rule base and wol...
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Aiming at solving the shortcomings of traditional wolf swarm algorithm, such as low search efficiency due to fixed search direction and radius, and easy to fall into local optimum when updating rules, an improved wolf...
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ISBN:
(纸本)9781538681787
Aiming at solving the shortcomings of traditional wolf swarm algorithm, such as low search efficiency due to fixed search direction and radius, and easy to fall into local optimum when updating rules, an improved wolf swarm algorithm is proposed in this paper. Firstly, the exploding rules of fireworks algorithm are introduced to reduce the exploring steps of wolves near prey and increase the searching direction in order to improve the ability of local exploration, while the wolves far from prey the searching radius are increased and the searching direction are decreased in order to improve the global search ability. Secondly, the moving step size of running behavior is improved automatically to adjust the step size according to the location of each wolf which can improve the efficiency of running behavior. Finally, the updating rules of wolf swarm algorithm is improved for the individual wolfswarm is selected according to probability to enhance the global optimization ability of the algorithm. The performance test in test function and path planning simulation experiment results show that the improved wolf swarm algorithm has faster convergence speed and higher convergence accuracy.
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