Abstract
Movie scenes scheduling problem (MSSP) is the NP-hard. It refers to the process of film shooting through the reasonable sequence of film scenes to minimize the total cost of film shooting. Studying scheduling problem of this kind is based on the location of the scene shooting, the cost of the scene transfer, the different remuneration of actors, and the different duration of the film scene shooting. The actors' waiting time and transfer cost during the shooting process are reduced as much as possible to make the total film shooting cost smaller. In this paper, an ILP(integer linear programming) model is established and a TABU search based method (TSBM), a particle swarm optimization based method (PSOBM) and an ant colony based method (ACOBM) are used to study the movie scenes scheduling problem. The objective is to compare and analyze relation performances of the three methods in the problem. By compared the experiment, the results show that TSBM, PSOBM and ACOBM can effectively reduce the total cost of film shooting. Comparison with the experiments show that the optimization result of ACOBM is better, the running time of TSBM is shorter, and the optimization result of TSBM is better than that of PSOBM. In addition, the running time of ACOBM is faster than that of PSOBM by setting the number of ants and the number of particles. The potential application of this study has great relevance for the optimization and ILP.
Highlights
In real life, due to the influence of various factors, the sequence of film scenes shooting can’t make the total cost of film optimal
This paper designs a TABU search based method (TSBM), particle swarm optimization based method (PSOBM) and ant colony based method (ACOBM) to solve the Movie scenes scheduling problem (MSSP) of the NP-hard
We compare the total cost of the movie from the TSBM and PSOBM to the total cost of the movie from the ACOBM
Summary
The PSOBM is a stochastic optimization parallel element heuristic algorithm based on swarm intelligence proposed by Eberhart and Dr Kennedy in 1995 This algorithm has a simple structure and fewer parameters to control, which has attracted the attention of domestic and foreign researchers. Salman et al [13] used the particle swarm optimization algorithm to study the task assignment problem of a Np-complete problem, and compared it with the probabilistic heuristic genetic algorithm based on population on the randomly generated task interaction graph. They proved the effectiveness of the PSOBM. We use the PSOBM to study a non-continuous integer linear programming model, and specify that the sequence of scene exchange represents the direction of particle search and the fitness value of the particle is the total cost value of the film under the shooting sequence
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
