A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems

An optimization spiking neural P system (OSNPS) aims to obtain the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators of evo-lutionary algorithms or swarm intelligence algorithms. To develop the promising and sig-nificant research direction, this paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) with a distributed population structure and a new adaptive learning rate considering population diversity. Extensive experiments on knapsack prob-lems show that DAOSNPS gains much better solutions than OSNPS, adaptive optimization spiking neural P system, genetic quantum algorithm and novel quantum evolutionary algo-rithm. Population diversity and convergence analysis indicate that DAOSNPS achieves a better balance between exploration and exploitation than OSNPS and AOSNPS. (c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) that can solve combinatorial optimization problems without the help of evolutionary algorithms or swarm intelligence algorithms. Extensive experiments demonstrate its superiority over other methods.