AN OPTIMIZATION SPIKING NEURAL P SYSTEM FOR APPROXIMATELY SOLVING COMBINATORIAL OPTIMIZATION PROBLEMS

TLDR

Membrane systems, or P systems, model cellular computation and cooperation, and spiking neural P systems extend this framework by integrating spiking neurons, while P systems have been employed to organize evolutionary operators in membrane‑inspired evolutionary algorithms for solving optimization problems. The study proposes a new P system design that directly approximates solutions to combinatorial optimization problems without relying on evolutionary operators. An extended spiking neural P system (ESNPS) is introduced, featuring probabilistic rule selection, multi‑neuron output, and a guider that adaptively adjusts rule probabilities, forming a family called optimization spiking neural P systems (OSNPS) to solve combinatorial optimization problems. Experiments on knapsack problems demonstrate the viability and effectiveness of the proposed system.

Abstract

Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.