首页|A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems
A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems
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NSTL
Elsevier
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.
Membrane computingSpiking neural P systemOptimization spiking neural P systemCombinatorial optimization problemsAUTOMATIC DESIGNEVOLUTION
NSTL
Elsevier
