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

Abstract

An optimization spiking neural P system (OSNPS) aims to obtain the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators of evolutionary algorithms or swarm intelligence algorithms. To develop the promising and significant 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 problems show that DAOSNPS gains much better solutions than OSNPS, adaptive optimization spiking neural P system, genetic quantum algorithm and novel quantum evolutionary algorithm. Population diversity and convergence analysis indicate that DAOSNPS achieves a better balance between exploration and exploitation than OSNPS and AOSNPS.