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

A multi-modal particle, multi-objective technology, applied in the field of optimization algorithms, which can solve problems such as uncertainty and problem dependencies

CN111814251BActive Publication Date: 2021-03-26BEIHANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2021-03-26

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Abstract

The invention discloses a multi-objective and multi-modal particle swarm optimization method based on Bayesian adaptive resonance, using Bayesian adaptive resonance theory to divide all particles into several populations; sort the particles; use the individual optimality of particles and the global optimality of the population to update the particles in the population; connect the non-dominated solution sets of various populations end to end to form a closed ring topology, and use particle swarm optimization based on ring topology The algorithm performs local exploration; repeat the above two update and exploration processes until the termination condition is satisfied, and output all non-dominated solution sets and Pareto frontiers. The present invention is applicable to the optimization of multi-objective and multi-modal problems. It can not only find the distribution of Pareto front in the target space, but also find the corresponding Pareto optimal solution set in the decision variable space, and provide a redundant backup method. , Improve the reliability of engineering practice activities.
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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...

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Embodiment Construction

[0091] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0092] The embodiment of the present invention discloses a multi-objective multi-modal particle swarm optimization method based on Bayesian adaptive resonance, the method steps are as follows figure 1 As shown, the specific process of the algorithm is as follows figure 2 As shown, the specific content is as follows:

[0093] Step 1: Determine the relevant parameters of the particle swarm and initialize the particle swarm.

[0094] The relevant parameters he...