Journal
INFORMATION SCIENCES
Volume 596, Issue -, Pages 1-14Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.03.007
Keywords
Membrane computing; Spiking neural P system; Optimization spiking neural P system; Combinatorial optimization problems
Categories
Funding
- National Natural Science Foundation of China [61972324, 61672437, 61702428]
- Sichuan Science and Technology Program [2021YFS0313, 2021YFG0133, 2020YJ0433, 2021YFN0104]
- Beijing Advanced Innovation Center for Intelligent Robots and Systems [2019IRS14]
- Artificial Intelligence Key Laboratory of Sichuan Province [2019RYJ06]
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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.
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.
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