Multi-objective multi-modal particle swarm optimization method based on Bayesian adaptive resonance

Abstract

The invention discloses a multi-objective multi-modal particle swarm optimization method based on Bayesian adaptive resonance. The method comprises the following steps: dividing all particles into a plurality of populations by using a Bayesian adaptive resonance theory; sorting the particles of each population according to a non-dominated sorting method and the special congestion distance; updating the particles in the population by using the individual optimization of the particles and the global optimization of the population; connecting the non-dominated solution sets of various groups endto end to form a closed loop topology, and performing local exploration by using a particle swarm optimization algorithm based on the loop topology; and repeating the two updating and exploring processes until a termination condition is met, and outputting all the non-dominated solution sets and the Pareto frontier. The method is suitable for optimization of solving the multi-target multi-modal problem, the distribution of the Pareto leading edge can be found in the target space, the corresponding Pareto optimal solution set can be found in the decision variable space, a redundant backup method is provided, and the reliability of engineering practice activities is improved.

Description

technical field [0001] The invention relates to the technical field of optimization algorithms, and more specifically relates to a multi-objective multi-mode particle swarm optimization method based on Bayesian adaptive resonance. Background technique [0002] In real life, there are often problems with multiple optimization objectives that conflict and restrict each other. For example, in the design process of an aircraft, it is necessary to ensure the stability of the aircraft and to pursue the maneuverability of the aircraft. Stability and maneuverability are two At the same time, the solution to the same goal may contain multiple solutions. For example, different aircraft design schemes may obtain the same stability and maneuverability. Such problems are called multi-objective multi-modal problems. The space composed of the objective function is called the target space, and the space composed of the variables used to generate the objective function is called the decision...

AI Technical Summary

Problems solved by technology

For multi-modal problems, niche particle swarm optimization algorithm is a better solution; for multi-objective problems, particle swarm optimization algorithms based on k-means clustering and Euclidean distance clustering have also been proposed, but now Some clustering-based particle swarm optimization algorithms often need to pre-set the number of clusters according to the number of solutions, but in reality it is difficult to know in advance how many solutions there are for multi-objective multi-modal functions, so the past methods generally use different Experiment with the number of clusters, and select a better number of clusters according to the experimental results, which brings great uncertainty and problem dependence to the optimization problem.

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