An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems

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.

Progressive preference articulation for decision making in multi-objective optimisation problems

“…In [22] an interactive algorithm based on R-NSGA-II is proposed, and in [23] R-NSGA-II is modified by integrating a stochastic local search in a memetic fashion, see [47], [10], [32], and [72].…”

Fuzzy Membrane Computing: Theory and Applications

“…These fuzzy production rules can be modeled by the following rFRSN P system Π 5 , as shown in Figure 8. (4) syn = {(1, 15), (2,15), (3,16), (4,16), (4,18), (5,16), (5,17), (5,18), (6,18), (7,17), (8,17), (9,17), (10,18), (15,11), (16,12), (17,13), (18,14)}.…”

“…(4) syn = {(1, 10) , (2, 10), (2,11), (3,11), (4,12), (5,12), (6, 13), (7,13), (8,13), (10,6), (11,7), (12,8), (13, 9)};…”

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

“…As a further complication, a well-known fact within the optimization community is that, as a consequence of the No Free Lunch Theorem, a universal optimizer does not exist, see [19]. On the contrary, a high performance in optimization is achieved by designing an optimizer around the problem features, see [20]- [23].…”