Showing papers by "Gexiang Zhang published in 2014"

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.

Dynamic Behavior Analysis of Membrane-Inspired Evolutionary Algorithms

Abstract: A membrane-inspired evolutionary algorithm (MIEA) is a successful in- stance of a model linking membrane computing and evolutionary algorithms This paper proposes the analysis of dynamic behaviors of MIEAs by introducing a set of population diversity and convergence measures This is the first attempt to obtain additional insights into the search capabilities of MIEAs The analysis is performed on the MIEA, QEPS (a quantum-inspired evolutionary algorithm based on membrane computing), and its counterpart algorithm, QIEA (a quantum-inspired evolutionary algorithm), using a comparative approach in an experimental context to better un- derstand their characteristics and performances Also the relationship between these measures and fitness is analyzed by presenting a tendency correlation coefficient to evaluate the importance of various population and convergence measures, which is beneficial to further improvements of MIEAs Results show that QEPS can achieve better balance between convergence and diversity than QIEA, which indicates QEPS has a stronger capacity of balancing exploration and exploitation than QIEA in order to prevent premature convergence that might occur Experiments utilizing knapsack problems support the above made statement

Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

Abstract: This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process.

Automatic design of deterministic and non-halting membrane systems by tuning syntactical ingredients.

Abstract: To solve the programmability issue of membrane computing models, the automatic design of membrane systems is a newly initiated and promising research direction. In this paper, we propose an automatic design method, Permutation Penalty Genetic Algorithm (PPGA), for a deterministic and non-halting membrane system by tuning membrane structures, initial objects and evolution rules. The main ideas of PPGA are the introduction of the permutation encoding technique for a membrane system, a penalty function evaluation approach for a candidate membrane system and a genetic algorithm for evolving a population of membrane systems toward a successful one fulfilling a given computational task. Experimental results show that PPGA can successfully accomplish the automatic design of a cell-like membrane system for computing the square of n ( n ≥ 1 is a natural number) and can find the minimal membrane systems with respect to their membrane structures, alphabet, initial objects, and evolution rules for fulfilling the given task. We also provide the guidelines on how to set the parameters of PPGA.

Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems

Abstract: This paper discusses the application of fuzzy reasoning spiking neural P systems with real numbers (rFRSN P systems) to fault diagnosis of electric locomotive systems. Relationships among breakdown signals and faulty sections in subsystems of electric locomotive systems are described in the form of fuzzy production rules firstly and then fault diagnosis models based on rFRSN P systems for these subsystems are built according to these rules. Fuzzy production rules for diagnosing electric locomotive systems are abstracted from the fault diagnosis analysis of the subsystems and the causality among faulty sections, faulty subsystems and electric locomotive systems. Finally, a diagnosis model based on rFRSN P systems for electric locomotive systems is proposed.

A many-objective evolutionary algorithm based on directional diversity and favorable convergence

Abstract: The performances of Pareto-based multi-objective evolutionary algorithms deteriorate severely when solving many-objective optimization problems (MaOPs) mainly due to the loss of selection pressure and inappropriate design in diversity maintenance mechanism. To handling MaOPs, this paper proposes a many-objective evolutionary algorithm (MaOEA) based on directional diversity and favorable convergence (MaOEA-DDFC). In the algorithm, the mating selection based on favorable convergence and Pareto-dominance is applied to strengthen the selection pressure while an environmental selection considering directional diversity and favorable convergence is designed in order to make a good trade-off between diversity and convergence. To validate algorithm performance, seven DTLZ problems with 3, 5, 7 and 10 objectives are tested. Experimental results show that the proposed MaOEA-DDFC performs better than five state-of-the-art MaOEAs in terms of inverted generational distance and hypervolume indicators.

Application of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railways

Abstract: This paper discusses the application of weighted fuzzy reasoning spiking neural P systems (WFRSN P systems) to fault diagnosis in traction power supply systems (TPSSs) of China high-speed railways. Four types of neurons are considered in WFRSN P systems to make them suitable for expressing status information of protective relays and circuit breakers, and a weighted matrix-based reasoning algorithm (WMBRA) is introduced to fulfill the reasoning based on the status information to obtain fault confidence levels of faulty sections. Fault diagnosis production rules in TPSSs and their WFRSN P system models are proposed to show how to use WFRSN P systems to describe different kinds of fault information. Building processes of fault diagnosis models for sections and fault region identification of feeding sections, and parameter setting of the models are described in detail. Case studies including normal power supply and over zone feeding show the effectiveness of the presented method.

A Population-P-Systems-Inspired Membrane Algorithm for Multi-objective Optimization

Abstract: This paper proposes a Population-P-Systems-inspired Membrane Algorithm (PPSMA) for multi-objective optimization. In the algorithm, the cells of population P systems are divided into two groups to implement different functions and the communications among cells are performed at two levels in order to obtain well converged and distributed solution set. Moreover, differential evolution is employed as search operator in PPSMA. Twelve multi-objective benchmark problems are utilized to test algorithm performance. Experimental results show that PPSMA performs better than five compared algorithms.