Particle swarm optimization method based on complex network

Abstract

The invention relates to a particle swarm optimization method based on a complex network. The particle swarm optimization method is used for solving the multiobjective optimization problem in the real world. The particle swarm optimization method based on the complex network comprises the steps that the population network topology is established according to a scale-free network generation mechanism, the optimization space, the population size, the positions of particles and the speeds of the particles are determined, the adaptive value is calculated according to a fitness function, the historical best position of each particle, the historical best position of the corresponding neighbor particle and the global historical best position of the particles are recorded, the positions and the speeds of the particles are updated in an iteration mode every time, the adaptive value is calculated again until iteration is completed, and the global best position is output. The particle swarm optimization method based on the complex network further provides four indexes for evaluating the optimal performance of center particles and non-center particles, the influence in neighborhood, the information transmission capacity, the advantages and disadvantages of the adaptive value and the capacity for maintaining population activeness. By means of the particle swarm optimization method based on the complex network, the local optimum can be effectively avoided, and the convergence rate and the optimization effect for resolving targets are balanced through the application of the particle swarm optimization algorithm.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a complex network-based particle swarm optimization method, which is used to solve multi-objective optimization problems in the real world, for example, in the field of air traffic control, to solve the problem of flight take-off and landing sequencing. Background technique [0002] Many optimization problems in the real world are multi-objective optimization problems, and the optimization result of multiple competing objectives is to obtain a set of feasible solutions. For example, multi-objective optimization of asset investment, optimization of vehicle routes for material transfer, optimal design of new products, product production scheduling, etc. For example, for the flight take-off and landing sorting problem, the solution space of the problem consists of all possible flight take-off and landing time series, and each point in the space is a time series (that is, a...

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Problems solved by technology

[0007] The present invention provides a novel particle swarm optimization method based on a complex network for the problem that the population network structure makes the convergence speed too fast or easily falls into a local optimum when PSO is used for multi-objective optimization at present.

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