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The K-means algorithm gets widely used due to its simplicity and effectiveness. But it is sensitive to the selection of initial cluster centres. In this paper, we proposed an initialisation method ...

The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.

Membrane computing is a fast developing natural computing field with wide applications. Cell-like P system is the one of membrane computing and it is a class of distributed and parallel computing model which is inspired from cell. Then, Shortest Path Faster Algorithm is another name of queue optimization algorithm for the Bellman-Ford algorithm. It is usually used to find single-source shortest paths with negative edges and negative weights. In this paper, we use rewrite rules and dissolution rules of cell-like P system, combined with queue thresholds to improve the SPFA algorithm. We use the average distance between the point where the queue is removed and the point to be queued as the threshold. The node whose distance is less than the threshold is inserted at the beginning of the queue, otherwise, the node is inserted at the end of the queue. A directional weighted digraph is applied to prove the feasibility of the algorithm in this paper. This method takes full advantage of the parallelism of the P system and finds the shortest path with fewer steps and shorter time.

Constrained optimization problems (COPs) which can be seen everywhere in life has been widely used in science and engineering. Given the complexity of this problem, it is difficult to solve them with traditional optimization techniques. In this paper, we have applied a new algorithm which is called Multi-population genetic algorithm (MPGA) based on an improved dynamic cell-like P system. This algorithm combines global search and local search, and have a significant effect on overcoming immature convergence problem. The sensitivity of calculated results to genetic control parameters is greatly reduced. We have also used Taguchi methods to select the initial population to reduce the number of iterations. The performance is tested on several well-known engineering design problems. This research can be applied in most practical problems and is of great help to optimize the operation.

The Spiking Neural P system is a branch of the neuronal-like P system in the membrane system with great parallelism. However, The Travelling Salesman Problem is a long-term NP-hard problem that finds the minimum costly Hamiltonian cycles in a weighted undirected graph. In this paper, we use the rules of division and dissolution of spiking neurons, combined with the idea of point-by-point traversal, we find all Hamiltonian cycles in weighted undirected graphs. Then computing by the binary form of the spike, resulting in the minimum cost Hamiltonian cycles. A bi-directional weighted digraph is applied to prove the feasibility of the algorithm in this paper. This method takes full advantage of the great parallelism of the SN P system, using fewer neurons and simpler rules and procedures to solve Travelling Salesman Problem.

BTK algorithm is an efficient algorithm for mining top-rank-k frequent patterns. It proposes new tree and list structures to store the information, employs subsume index concept, early pruning strategy and threshold raising method to reduce the search space. In this paper, a fast BTK algorithm, called CP-BTK algorithm is proposed, which is based on cell-like P system with active membranes. Cell-like P system is new computing model inspired from biological cells, operations in cell-like P system are distributed and parallel, so it can save time and improve the efficiency of algorithm greatly. And finally a example is given to illustrate the practicability and effectiveness of the proposed algorithm.

In this paper an improved ranked K-medoids algorithm by a specific cell-like P system is proposed which extends the application of membrane computing. First, we use the maximum distance method to choose the initial clustering medoids, maximum distance method which is based on the fact that the farthest initial medoids were the least likely assigned in the same cluster. And then, we realize this algorithm by a specific P system. P system is adequate to solve clustering problem for its high parallelism and lower computational time complexity. By computation of the designed system, one possible clustering result is obtained in a non-deterministic and maximal parallel way. Through example verification, our algorithm can improve the quality of clustering.

A membrane system for decision-making problems is proposed in this paper to provide new ideas for parallel solutions in today’s big data environment. Because a membrane system has great parallelism, it could reduce the calculation time complexity and is suitable for solving some complex problems such as decision-making problems. First, the possibility of such a problem’s parallelism is analyzed, and then the membrane system is established with all its rules to solve the problem. Finally, an example is given to prove its feasibility. This represents another attempt in the application of a membrane system.

Hypercube is the most important and attractive network communication model that is widely used in parallel computing and routing algorithm. And spiking neural P system is currently much investigated in membrane computing. The purpose of this paper is to propose a new solution using SN P systems to the broadcast routing problem. Two SN P systems are constructed for dealing with the broadcast problem in n dimensional hypercube with or without faulty nodes. Some examples are given to show the effectiveness of the algorithm. The broadcast technique provides a new thought comparing to traditional broadcast algorithms.

In this paper, we propose a new technique for clustering, which is the combination of membrane computing and Bin-packing technique. A new kind of P system (graph P system) is constructed which is different with the traditional ones. New rules and membrane structure are described. Cluster analysis is transformed by Bin-packing technique. All processes of the algorithm are implemented on this P system, including rules for mutation, swap and the calculation of energy change. Finally we apply the technique in the heart disease data set and obtain some results.

P systems are biologically inspired theoretical models of distributed and parallel computing. Hamilton path is a classical NP problem, recently, there are lots of methods to solve it. Today we give a new and efficient algorithm to this classic. This paper uses the improved P system with priority and promoters/inhibitors to give an efficient solution to Hamilton path problem. We give two examples to illustrate our method’s feasibility. We discuss future research problems also.