An optimization numerical spiking neural P system for solving constrained optimization problems

An optimization spiking neural P (OSN P) system is a discrete optimization model without the aid of evolutionary operators of evolutionary algorithms or swarm intelligence algorithms. However, since the processing object of OSN P systems is a spike, where information is encoded by the timing of spikes or the number of spikes in neurons, OSN P systems are limited for solving continuous optimization problems. To break this limitation, an extended numerical spiking neural (ENSN P) system is proposed based on numerical spik-ing neural P (NSN P) systems and multiple (ENSN P) systems, called optimization numer-ical spiking neural P systems (ONSN P systems or ONSNPS), are designed to solve continuous constrained optimization problems. More specifically, in ENSN P systems, the production functions are selected by probability to achieve updated parameters. In OSN P systems, a guider algorithm is introduced to finish individuals' crossover and selection. The extensively experimental results in five benchmarks, thirty-two optimization prob-lems including five benchmark problems, seventeen manufacturing design optimization problems and ten benchmarks from CEC show that ONSN P systems in this paper outper-form or are competitive to twenty-eight optimization algorithms. Finally, algorithm complexity and Holm-Bonferroni procedure based on statistical results is used to test the complexity changing when we use different dimensionality of the search space and the dif-ference in terms of statistical performance. The testing results indicate that the time complexity of ONSN P systems grows linearly with problem dimensions and ONSN P systems are better performance than the most algorithms.(c) 2023 Elsevier Inc. All rights reserved.

Automated summary

An extended numerical spiking neural (ENSN P) system is proposed to solve continuous constrained optimization problems. In ENSN P systems, production functions are selected by probability to achieve updated parameters. Experimental results show that the proposed method outperforms or is competitive with other 28 optimization algorithms in five benchmarks.