Amphibious vehicle layout area utilization maximization method based on multi-strategy dynamic adjustment

Abstract

The invention relates to an amphibious vehicle layout area utilization maximization method based on multi-strategy dynamic adjustment, and the method comprises the steps: obtaining the related information of a vehicle cabin deck and a vehicle, determining a constraint condition, determining an objective function, employing decimal coding to code the vehicle, initializing parameters, randomly generating a vehicle layout sequence to form an initial population, calculating fitness values of individuals, storing the optimal individuals, judging whether the maximum number of iterations is reached or not, and dynamically adjusting the scales of the three sub-populations according to three different evaluation strategies and the selection probability of dynamic adjustment; and performing constrained crossover or annular crossover on the optimal individual and all individuals in the sub-populations, performing mutation operation by adopting a dynamically adjusted mutation probability, selecting effective evolution individuals in the three sub-populations to form a new population, and decoding the optimal individual of the last generation to obtain an optimal layout diagram. The method hasthe advantages that the optimal layout drawing can be quickly solved, and the layout area of the amphibious vehicle can be utilized to the maximum extent.

Description

technical field [0001] The invention belongs to the field of vehicle layout in amphibious assault ship vehicle cabins, and solves the problem of maximizing the layout area utilization of amphibious vehicles by combining the improved minimum horizontal line method, multi-strategy dynamic adjustment selection, mutation operator and subpopulation size genetic algorithm. Background technique [0002] The amphibious assault ship plays a very important role in the process of amphibious operations. The main function of the amphibious assault ship is the transportation of personnel and weapons and equipment. Because the displacement is more than tens of thousands of tons, it can carry a large amount of personnel and materials, including helicopters and military vehicles. It has flexible and powerful force projection capabilities. When an amphibious assault ship performs amphibious operations in non-combat loading mode, it needs to take the highest utilization rate of the deck area o...

AI Technical Summary

Problems solved by technology

In the process of placing rectangular parts, when the rectangular parts cannot be discharged into the lowest horizontal line and whether there are rectangular parts that can be discharged into the current lowest horizontal line among the subsequent undischarged rectangular parts, it will lead to waste of free space that can be used, affecting the final layout effect

[0005] (2) Vehicle arrangement optimization algorithm, the genetic algorithm adopting adaptive selection strategy in the early stage of evolution, increases the probability of individuals with high fitness value being selected, and reduces the probability of individuals with low fitness value being selected, making the selection method excessive Preferring to individuals with strong adaptability, the population may converge to a local optimal solution too quickly to fully explore the search space, resulting in premature algorithm

[0006] (3) In the late stage of population evolution of genetic algorithm, the difference in individual fitness value is very small, and the use of sequential crossover method may lead to invalid crossover and repeated individuals

[0007] (4) The mutation probability of the genetic algorithm adopts a fixed value, which reduces the space search ability of the algorithm, resulting in the final result being a local optimum

[0008] (5) In the genetic algorithm, based on the relative fitness value between individuals, individuals are selected according to the probability for reorganization, so that the search of the algorithm has no directionality, and the fitness value of generating new individuals is not high

[0009] In addition, the heuristic information value evaluation rule in the patent "A Method and System for Rectangular Intelligent Nesting Based on Knowledge Transfer" only considers the alignment of the rectangle and the height of both sides, and does not consider the ratio of the width of the rectangle to be arranged to the width of the lowest horizontal line the elements of

The paper "Research on Improved Genetic Algorithm Based on Reinforcement Learning" adopts double-elite evolutionary genetic algorithm, and only two individual evaluation strategies are used. The search ability and global convergence of the algorithm are not high; The probability is not high; use reinforcement learning to adjust the probability of crossover and mutation, increase the calculation amount of the algorithm, and make the convergence speed of the algorithm slow

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