Information Interaction Mechanism Research Articles

A novel artificial bee colony algorithm with local and global information interaction

The artificial bee colony algorithm (ABC) is a new stochastic and population-based optimization method, which has been attracting a great deal of attention, due to its simple structure, easy implementation and outstanding performance. However, it also suffers from slow convergence like other evolutionary algorithms. In order to address this concerning issue, in this paper, we propose a novel artificial bee colony algorithm with local and global information interaction, called ABCLGII. In employed bee phase, each employed bee is designed to learn from the best individual among its neighbors or in a local visible scope. By this way, the search of employed bees is no longer independent and blind, but is cooperative and directional, such that a local information interaction mechanism is conducted between employed bees. In onlooker bee phase, only a part of superior food sources have chance to attract onlooker bees to exploit in their vicinity. Moreover, two novel search equations are proposed for onlooker bees to generate candidate food sources. Specifically, one exploits the useful information of some good solutions, while the other combines the valuable information of the current best solution and some good solutions simultaneously. An adaptive selection mechanism is accordingly designed for onlooker bees to choose a proper search equation for producing candidate food sources. In this way, a global information interaction mechanism is employed for onlooker bees. In order to evaluate the performance of ABCLGII, we compare ABCLGII with the original ABC and other outstanding ABC variants on 52 frequently used test functions. The experimental results show that ABCLGII is better than or at least competitive to the state-of-the-art ABC variants in terms of solution quality, robustness and convergence speed.

Intelligent particle filter based on bat algorithm

Particle filer is apt to have particle impoverishment with unstable filtering precision, and a large number of granules are required to estimate the nonlinear system accurately, which reduces the comprehensive performance of the algorithm. To solve this problem, a new particle filter based on bat algorithm is presented in this paper, where particles are used to represent individual bat so as to imitate the search process of bats for preys. In traditional resampling process, particles are directly discarded, the improved algorithm adopts another approach and solves the problem of particle impoverishment. It combines the advantages of particle swarm optimization algorithm and harmonic algorithm perfectly. New particle filter has capacity of global and local search and is superior in computation accuracy and efficiency. By adjusting frequency, loudness, and impulse emissivity of particle swarm, the optimal particle at that time is followed by particle swarm to search in the solution space. The global search and local search can be switched dynamically to improve the overall quality of the particles swarm as well as the distribution rationality. In addition, the improved particle filter uses Lvy flight strategy to avoid being attracted by harmful local optimal solution, it expands the space of research and further promotes the optimization effect of particle distribution. Using the useful information about particle swarm, improved particle filter can make particles get rid of local optimum and reduce the waste of iterations in insignificant status change. Based on the number of valid particle samples, it can improve quality of particle samples by expanding their diversity. In information interaction mechanism of improved particle filter, the method in this paper sets scoreboard of particle target function to compare the value of particle target function at each iteration sub-moment with the value of target function on scoreboard to gain global optimum of all particles at current filtering moment. Taking information interaction between global optimum and particle swarm, the guiding function of global optimum is realized. The process of particle optimization is ended prematurely through setting a maximum iteration or termination threshold. There is a tendency for the whole particle swarm closing to high likehood area without global convergence so that the advantages of improved particle filter in accuracy and speed will not be damaged. In addition, convergence analysis and computational complexity analysis are given in this paper. Experiment indicates that this method can improve the particle diversity and prediction accuracy of particle filter, and meanwhile reduce the particle quantity obviously which is required by the state value prediction for nonlinear system.

Moduli-based language for building problem-solving scenarios

We propose a language for executive specification of problem-solving scenarios. The scenarios are an aggregate of actions realized as independent software moduli, the information connections between them and the sequence of their execution. An executive system controlling the execution of the scenarios was developed on the basis of the language. This system is the kernel of a system for automated control system design. The scenarios have a hierarchical structure. The organization of the scenarios is based on an object-oriented paradigm. Every module is an instance of a particular type module, called class. This allows one and the same module to be executed in different contexts. A unique information interaction mechanism between the moduli in the scenarios, allowing on-line changes in the information flow, was developed. The language is problem-independent, and can be used in various problem domains.