Adaptive dynamic cooperative particle swarm optimization method for feed-forward network

Abstract

The invention discloses an adaptive dynamic cooperative particle swarm optimization method for a feed-forward network. The method comprises the following steps of (1) initializing particles, and initializing sub-populations; (2) recording extreme values (pi) of particle individuals, an extreme value (pn) of each sub-population and a global extreme value (pg); (3) sorting the sub-populations according to the goodness of the extreme value (pn) of each sub-population, and determining the number of cooperative particles of the sub-populations; (4) enabling the particles in the sub-populations to learn from the extreme values (pi) of the particles and the extreme values (pn) of the sub-populations; (5) randomly selecting the cooperative particles from other sub-populations, enabling the cooperative particles to learn from the extreme values (pi) of the cooperative particles and the extreme values (pn) of the sub-populations, and updating the extreme values of the cooperative particles and social extreme values of the sub-populations to which the cooperative particles belong at any time according to the goodness of the particles; (6) updating particle information in sequence according to the steps (4 and 5), and randomly selecting at most two particles from a worse sub-population to enable the particles to become members of a better sub-population; (7) meeting all stopping conditions, stopping calculation, and otherwise, carrying out the step (2) for next iteration. By virtue of the method, the learning capacity and identification capacity of a network can be improved.

Description

technical field [0001] The present invention belongs to the field of pattern classification. This paper designs a dynamic adaptive cooperative particle swarm algorithm to optimize the feed-forward network, which has a wide range of applications in the fields of face recognition, smiley face recognition, and gender recognition. Background technique [0002] With its self-learning, self-organization, fault-tolerant characteristics and the ability to simulate nonlinear relationships, neural networks are particularly suitable for solving pattern classification problems. Hornik demonstrated that a three-layer feed-forward neural network can model complex nonlinear relationships with arbitrary precision [Ref. 1]. The realization of the above-mentioned performance of the neural network depends on the sufficient training of the neural network structure and weights under the guidance of a good objective function, so as to realize the law of the entire population in a limited learnin...

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Problems solved by technology

The algorithm adopts this kind of objective function, which is easy to fall into

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