Xinhong Hei

GI/Geom/1 queue based on communication model for mesh networks

In mesh networks architecture, it should be permitted to visit the mobile client points. Whereas in mesh networks environment, the main throughput flows usually communicate with the conventional wired network. The so-called gateway nodes can link directly to traditional Ethernet, depending on these mesh nodes, and can obtain access to data sources that are related to the Ethernet. In wireless mesh networks WMNs, the quantities of gateways are limited. The packet-processing ability of settled wireless nodes is limited. Consequently, throughput loads of mesh nodes highly affect the network performance. In this paper, we propose a queuing system that relied on traffic model for WMNs. On the basis of the intelligent adaptivenes, the model considers the influences of interference. Using this intelligent model, service stations with boundless capacity are defined as between gateway and common nodes based on the largest hop count from the gateways, whereas the other nodes are modeled as service stations with certain capacity. Afterwards, we analyze the network throughput, mean packet loss ratio, and packet delay on each hop node with the adaptive model proposed. Simulations show that the intelligent and adaptive model presented is precise in modeling the features of traffic loads in WMNs. Copyright

Survey A review of opposition-based learning from 2005 to 2012

Diverse forms of opposition are already existent virtually everywhere around us, and utilizing opposite numbers to accelerate an optimization method is a new idea. Since 2005, opposition-based learning is a fast growing research field in which a variety of new theoretical models and technical methods have been studied for dealing with complex and significant problems. As a result, an increasing number of works have thus proposed. This paper provides a survey on the state-of-the-art of research, reported in the specialized literature to date, related to this framework. This overview covers basic concepts, theoretical foundation, combinations with intelligent algorithms, and typical application fields. A number of challenges that can be undertaken to help move the field forward are discussed according to the current state of the opposition-based learning.

Teaching–learning-based optimization with dynamic group strategy for global optimization

Abstract Global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence. In recent years, there have been some significant developments in these areas regarding the solution of global optimization problems. In this paper, we propose an improved teaching–learning-based optimization (TLBO) algorithm with dynamic group strategy (DGS) for global optimization problems. Different to the original TLBO algorithm, DGSTLBO enables each learner to learn from the mean of his corresponding group, rather than the mean of the class, in the teacher phase. Furthermore, each learner employs the random learning strategy or the quantum-behaved learning strategy in his corresponding group in the learner phase. Regrouping occurs dynamically after a certain number of generations, helping to maintain the diversity of the population and discourage premature convergence. To verify the feasibility and effectiveness of the proposed algorithm, experiments are conducted on 18 numerical benchmark functions in 10, 30, and 50 dimensions. The results show that the proposed DGSTLBO algorithm is an effective method for global optimization problems.

Multi-objective optimization using teaching-learning-based optimization algorithm

Two major goals in multi-objective optimization are to obtain a set of nondominated solutions as closely as possible to the true Pareto front (PF) and maintain a well-distributed solution set along the Pareto front. In this paper, we propose a teaching-learning-based optimization (TLBO) algorithm for multi-objective optimization problems (MOPs). In our algorithm, we adopt the nondominated sorting concept and the mechanism of crowding distance computation. The teacher of the learners is selected from among current nondominated solutions with the highest crowding distance values and the centroid of the nondominated solutions from current archive is selected as the Mean of the learners. The performance of proposed algorithm is investigated on a set of some benchmark problems and real life application problems and the results show that the proposed algorithm is a challenging method for multi-objective algorithms.

An improved teaching-learning-based optimization with neighborhood search for applications of ANN

Teaching-learning-based optimization (TLBO) algorithm, which simulates the teaching-learning process of the class room, is one of the recently proposed swarm intelligent (SI) algorithms. The performance of TLBO is maintained by the teaching and learning process, but when the learners cannot found a better position than the old one at some successive iteration, the population might be trapped into local optima. In this paper, an improved teaching-learning-based optimization algorithm with neighborhood search (NSTLBO) is presented. In the proposed method, a ring neighborhood topology is introduced into the original TLBO algorithm to maintain the exploration ability of the population. Different than the traditional method to utilize the global information, the mutation of each learner is now restricted within a certain neighboring area so as to fully utilize the whole space and avoid over-congestion around local optima. Moreover, a mutation operation is presented to NSTLBO during the duplicate eliminations in order to maintain the diversity of population. To verify the performance of the proposed algorithm, thirty-two benchmark functions are utilized. Finally, three application problems of artificial neural network are examined. The results in thirty-two benchmark functions and three applications of ANN indicate that the proposed algorithm has shown interesting outcomes.

A hybridization of teaching---learning-based optimization and differential evolution for chaotic time series prediction

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching–learning-based optimization (TLBO)–differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO–DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.

Teaching-learning-based optimization with learning experience of other learners and its application

The learning experience of some other learners is introduced into TLBO so as to improve its performance.We design a random learning strategy whatever in the Learner Phase or in the Teacher Phase.18 benchmark functions and two real-world problems are used in experimental study.The results indicate that the proposed algorithm has shown interesting outcomes. To improve the global performance of the standard teaching-learning-based optimization (TLBO) algorithm, an improved TLBO algorithm (LETLBO) with learning experience of other learners is proposed in the paper. In LETLBO, two random possibilities are used to determine the learning methods of learners in different phases. In the Teacher Phase, the learners improve their grades by utilizing the mean information of the class and the learning experience of other learners according to a random probability. In Learner Phase, the learner learns knowledge from another learner which is randomly selected from the whole class or the mutual learning experience of two randomly selected learners according to a random probability. Moreover, area copying operator which is used in Producer-Scrounger model is used for parts of learners to increase its learning speed. The feasibility and effectiveness of the proposed algorithm are tested on 18 benchmark functions and two practical optimization problems. The merits of the improved method are compared with those of some other evolutionary algorithms (EAs), the results show that the proposed algorithm is an effective method for global optimization problems.

Parameter identification of chaotic systems using artificial raindrop algorithm

Abstract The knowledge about parameter identification is an important issue for the synchronization and control of chaos. These chaotic systems involve some input parameters which may greatly affect their dynamic behavior. In this paper, a newly developed meta-heuristic method – artificial raindrop algorithm (ARA) inspired from the phenomenon of natural rainfall, is applied to identify the unknown parameters of chaotic system first time in the literature. In order to verify the effectiveness of ARA, numerical experiments are carried on Chen, L u ¨ , R o ¨ ssler, Lorenz system, Logistic with time-delay system, and Mackey–Glass with time-delay system. The simulation results indicate that ARA can more effectively and accurately identify the parameters for given chaotic systems with respect to other seven state-of-the-art intelligent optimization algorithms.

Bare-Bones Teaching-Learning-Based Optimization

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs

Brain Storm Optimization (BSO) algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.