Multi-learning rate optimization spiking neural P systems for solving the discrete optimization problems. (English) Zbl 1518.68106
Summary: To further improve the performance of optimization spiking neural P system (OSNPS), a multi-learning rate optimization spiking neural P system (MLOSNPS) is proposed. More specifically, by borrowing the distributed population structure of DAOSNPS, the distributed population structure with multiple subpopulations, single migration individual and information exchange considering convergence and diversity is adopted in MLOSNPS. In addition, three different learning rates in OSNPS, AOSNPS and DAOSNPS are used at different evolutionary stages in MLOSNPS. The experimental results in 0/1 knapsack problems show that MLOSNPS achieves a better balance between exploration and exploitation than OSNPS, AOSNPS and DAOSNPS.
MSC:
| 68Q07 | Biologically inspired models of computation (DNA computing, membrane computing, etc.) |
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 90C27 | Combinatorial optimization |
Keywords:
discrete optimization problems; optimization spiking neural P-systems; spiking neural P-systems; membrane computingReferences:
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