Jian Cheng

A novel multi-population cultural algorithm adopting knowledge migration

In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However, migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. In order to enhance the migration efficiency, a novel multi-population cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating knowledge among sub-populations at the constant intervals, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions with high-dimension as the examples, simulation results indicate that the algorithm can effectively improve the speed of convergence and overcome premature convergence.

Multi-population cooperative cultural algorithms

Based on the dual structure of culture algorithm, a multi-population cooperative cultural algorithm is proposed by embedding the competition cooperative genetic algorithm into the population space of culture algorithm. In each sub-population, genetic algorithm is adopted. And its population size is adjusted in terms of population density so as to enhance the diversity. In belief space, each kind of the knowledge extracted from best individual of all sub-population is utilized to induce each sub-populations mutation operator. Simulation results indicate that this algorithm can effectively speed up the convergence and improve the accuracy and stability of the solutions.

Optimal Design of Passive Power Filters Based on Knowledge-Based Chaotic Evolutionary Algorithm

Design of passive power filters shall meet the demand of harmonics suppression effect and economic target. However, traditional experience-based method has difficulty achieving the optimal solution because it only takes technology target into account. To solve the problem, two objectives including minimum total harmonics distortion of current and minimum cost for equipments are constructed. Taken capacitors in passive power filter as variables, such non-dominant objectives are transformed into single weighted objective. In order to achieve the optimal solution effectively, a novel evolutionary algorithm with knowledge-based chaotic mutation is proposed. The scale of mutation is adaptively adjusted based on logistic chaotic sequence according to current implicit knowledge describing the dominant space. Taken three-phase full wave controlled rectifier as harmonic source, simulation results show that filter designed by the proposed algorithm have better harmonics suppression effect and lower investment for equipments than filter designed by experience-based method.

A Distributed Support Vector Machines Architecture for Chaotic Time Series Prediction

Chaos limits predictability so that the prediction of chaotic time series is very difficult. Originated from the idea of combining several models to improve prediction accuracy and robustness, a new approach is presented to model and predict chaotic time series based on a distributed support vector machines in the embedding phase space. A three-stage architecture of the distributed support vector machines is proposed to improve its prediction accuracy and generalization performance for chaotic time series. In the first stage, Fuzzy C-means clustering algorithm is adopted to partition the input dataset into several subsets. Then, in the second stage, all the submodels are constructed by least squares support vector machines that best fit partitioned subsets, respectively, with Gaussian radial basis function kernel and the optimal free parameters. A fuzzy synthesis algorithm is used in the third stage to combine the outputs of submodels to obtain the final output, in which the degrees of memberships are generated by the relationship between a new input sample data and each subset center. All the models are evaluated by coal mine gas concentration in the experiment. The simulation shows that the distributed support vector machines achieves significant improvement in the generalization performance and the storage consumption in comparison with the single support vector machine model.

Knowledge-Inducing interactive genetic algorithms based on multi-agent

Interactive genetic algorithms lack a common model to effectively integrate different assistant evolution strategies including knowledge-based methods and fitness assignment strategies.Aiming at the problems,knowledge-based interactive genetic algorithm based on multi-agent is put forward in the paper combined with the flexibility of multi-agent systems.Five kinds of agents are abstracted based on decomposed-integral strategy of MAS.A novel implicit knowledge model and corresponding inducing strategy are proposed and realized by knowledge-inducing agent.A novel substitution strategy for evaluating fitness by an online model instead of human is proposed and implemented in fitness-estimation agent.State-switch conditions of above agents are given using agent-oriented programming. Taking fashion design system as a testing platform, the rationality of the model and the effective of assistant evolution strategies proposed in the paper are validated. Simulation results indicate this algorithm can effectively alleviate users fatigue and improve the speed of convergence.

Path planning method for robots in complex ground environment based on cultural algorithm

In complex ground environment, different regions have different road conditions. Path planning for robots in such environment is an open problem, which lacks effective methods. A novel global path planning method based on common sense and evolution knowledge is proposed by adopting dual evolution structure in culture algorithms. Common sense describes ground information and feasibility of environment, which is used to evaluate and select the paths. Evolution knowledge describes the angle relationship between the path and the obstacles, or the common segments of paths, which is used to judge and repair infeasible individuals. Taken two types of environments with different obstacles and road conditions as examples, simulation results indicate that the algorithm can effectively solve path planning problem in complex ground environment and decrease the computation complexity for judgment and repair of infeasible individuals. It also can improve the convergence speed and have better computation stability.

A Novel Multi-agent Based Complex Process Control System and Its Application

Complex process control systems need a hybrid control mode, which combines hierarchical structure with decentralized control units. Autonomy of agents and cooperation capability between agents in multi-agent system provide basis for realization of the hybrid control mode. A novel multi-agent based complex process control system is proposed. Semantic representation of a control-agent is presented utilizing agent-oriented programming. A novel temporal logic analysis of a control-agent is proposed using Petri nets. Collaboration relationships among control-agents are analyzed based on extended contract net protocol aiming at the lack of reference [1]. Taken pressure control of recycled gas with complicated disturbances as an application, five kinds of control-agents are derived from control-agent. Reachable marking tree and different transition of each derived control-agent are analyzed in detail. Actual running effect indicates multi-agent based hybrid control mode is rationality and flexible. Temporal logic analysis based on Petri nets ensures the reachability of the systems. Extended contract net protocol provides a reasonable realization for collaboration relationships.

Coke oven heating temperature fuzzy control system

Coke oven heating temperature is reflected by flue temperature and adjusted by gas flow. According to the analysis of factors which influence flue temperature, coke oven heating temperature fuzzy control strategy is introduced, which is made of flue temperature fuzzy control in outer loop and gas flow PID control in inner loop. Actual run results show that this strategy can solve the large inertia between flue temperature and gas flow adjustment effectively and shorten adjust time. Hence, this strategy satisfies the actual production needs.

Supervised isomap based on pairwise constraints

Most existing typical dimension reduction methods, for example Isomap algorithm, are hard to deal with the problem of violation of pairwise constraint. In this paper, a pairwise-constraint supervised Isomap algorithm (PC-SIsomap) is proposed, in which the supervised information is taken on the form of pairwise constraint introduced to geodesic distance. Mapping high-dimensional and non-linear data points to low-dimensional embedding space, PC-SIsomap can effectively take advantage of pairwise constraint information to realize dimensionality reduction. At the same time in order to solve the out-of-sample problem in manifold learning, BP neural network is employed to build a nonlinear mapping relation from the high-dimensional original data space to a low-dimensional feature space. Consequentially, support vector machine (SVM) classifiers are designed for realizing pattern classification in the low-dimensional feature space. Some experiments are executed in UCI datasets and dataset of gas safety monitoring system in coal mine, the results show that PC-SIsomap not only reduces the residual value, but also improves the classification accuracy.

Multi-spectral remote sensing images classification method based on adaptive immune clonal selection culture algorithm

In immune clonal selection algorithm for remote sensing images classification problem, only clonal selection mechanism is adopted. It makes the exploitation and exploration of the algorithm limited. To solve above problem, adaptive immune clonal selection culture algorithm is introduced in the paper. It fully uses the dual evolution mechanism of culture algorithm to extract implicit knowledge in belief space. According to the evolution situation noted in topological knowledge, a hybrid selection strategy integrating clonal selection and (