Membrane system is a computing model which imitates natural process at cellular level. In this system all objects can evolve in a maximal parallelism and distributed manner. Recent results show that this model is a promising framework for solving NPcomplete problems in polynomial time. The paper proves the possibility to perform operations with integer numbers in a membrane system, and gives an effective method to implement arithmetic operations, which seems to have a lower complexity than when implementing them in usual computer architecture

Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, resulting in large memory occupancy and high calculation delay. Thus, this paper proposes a low-delay lightweight recurrent neural network (LLRNN) model for mechanical fault diagnosis, based on a special LSTM cell structure with a forget gate. The input vibration signal is segmented into several shorter sub-signals in order to shorten the length of the time sequence. Then, these sub-signals are sent into the network directly and converted into the final diagnostic results without any manual participation. Compared with some existing methods, our experiments illustrate that the proposed method has less memory space occupancy and lower computational delay while maintaining the same level of accuracy.

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Since the membrane algorithm was proposed, it has been used for many optimization problems such as, traveling salesman problem, the knapsack problem, and so on. In membrane algorithms, the membranes have two functions: container and comparator. As a container, each membrane contains one evolutionary algorithm like genetic algorithm and ant colony algorithm. These algorithms are called sub-algorithms and used to evolve individuals. As a comparator, the membrane will compare the results of sub-algorithms, and select the best as the base of the next evolvement. This paper proposes a novel evolutionary algorithm called membrane evolutionary algorithm framework (MEAF). Unlike the presented membrane algorithms, the membranes in MEAF will be evolved to solve problems by using four operators that are abstracted from the life cycle of living cells. Based on MEAF, a membrane evolutionary algorithm called MEAMVC is proposed to solve the minimum vertex cover (MVC) problem. The experimental results show the advantages of MEAMVC when MEAMVC is compared with two state-of-the-art MVC algorithms proposed in recent years.

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Ant Colony Optimization algorithms often suffer from criticism for the local optimization and premature convergence. In this paper, we introduce several main ant algorithms, analyze their design ideas, and draw the conclusion that biases in transition rules and update rules are the root cause of the local optimization and premature convergence. Inspired by the adaptive behaviors of some Monomorium ant species in the real world, we design a novel transition rule to overcome the existing problems of ACO algorithms. Moreover, applying the new transition rule, we propose an improved version of Ant System-Moderate Ant System. This improved algorithm is experimentally turned out to be effective and competitive.

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