A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems
•Proposes a distributed adaptive optimization spiking neural P system 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 and more stable solutions than OSNPS, AOSNPS...
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01.06.2022
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| Abstract | •Proposes a distributed adaptive optimization spiking neural P system 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 and more stable solutions than OSNPS, AOSNPS and other two optimization algorithms.
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. |
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| AbstractList | •Proposes a distributed adaptive optimization spiking neural P system 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 and more stable solutions than OSNPS, AOSNPS and other two optimization algorithms.
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. |
| Author | Zhou, Kang Rong, Haina Guo, Dequan Zhang, Gexiang Yang, Qiang Luo, Biao Dong, Jianping Zhu, Ming |
| Author_xml | – sequence: 1 givenname: Jianping surname: Dong fullname: Dong, Jianping organization: School of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China – sequence: 2 givenname: Gexiang surname: Zhang fullname: Zhang, Gexiang email: zhgxdylan@126.com, 15775960380@163.com organization: Research Center for Artificial Intelligence, Chengdu University of Technology, Chengdu 610059, China – sequence: 3 givenname: Biao surname: Luo fullname: Luo, Biao organization: Research Center for Artificial Intelligence, Chengdu University of Technology, Chengdu 610059, China – sequence: 4 givenname: Qiang surname: Yang fullname: Yang, Qiang organization: School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China – sequence: 5 givenname: Dequan surname: Guo fullname: Guo, Dequan organization: School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China – sequence: 6 givenname: Haina surname: Rong fullname: Rong, Haina organization: School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China – sequence: 7 givenname: Ming surname: Zhu fullname: Zhu, Ming organization: School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China – sequence: 8 givenname: Kang surname: Zhou fullname: Zhou, Kang organization: School of Department of Economics and Management, Wuhan Polytechnic University, Wuhan, 430023, China |
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