Yuanxiang Li

A novel differential evolution scheme combined with particle swarm intelligence

Differential evolution (DE) and particle swarm optimization (PSO) are the evolutionary computation paradigms, and both have shown superior performance on complex nonlinear function optimization problems. This paper detects the underlying relationship between them and then qualitatively proves that the two heuristic approaches from different theoretical background are consistent in form. Within the general perspective, the PSO can be regarded as a kind of DE. Inspired by this, a novel variant of DE mixed with particle swarm intelligence (DE-SI) is presented. Comparison experiments involving ten test functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the DE-SI, two versions of DE and two PSO variants. The results from our study show that DE-SI keeps the most rapid convergence rate of all techniques and obtains the global optima for most benchmark problems.

A multiobjective box-covering algorithm for fractal modularity on complex networks

Abstract The box-covering method is widely used on measuring the fractal property on complex networks. The problem of finding the minimum number of boxes to tile a network is known as a NP-hard problem. Many algorithms have been proposed to solve this problem. All the current box-covering algorithms regard the box number minimization as the only objective. However, the fractal modularity of the network partition divided by the box-covering method, has been proved to be strongly related to the information transportation in complex networks. Maximizing the fractal modularity is also important in the box-covering method, which can be divided into two objectives: maximization of ratio association and minimization of ratio cut. In this paper, to solve the dilemma of minimizing the box number and maximizing the fractal modularity at the same time, a multiobjective discrete particle swarm optimization box-covering (MOPSOBC) algorithm is proposed. The MOPSOBC algorithm applies the decomposition approach on the two objectives to approximate the Pareto front. The proposed MOPSOBC algorithm has been applied to six benchmark networks and compared with the state-of-the-art algorithms, including two classical box-covering algorithms, four single objective optimization algorithms and six multiobjective optimization algorithms. The experimental results show that the MOPSOBC algorithm can get similar box numbers with the current best algorithm, and it outperforms the state-of-the-art algorithms on the fractal modularity and normalized mutual information.

Automated analog circuit design using two-layer genetic programming

Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a two-layer evolutionary scheme based on genetic programming (GP), which uses a divide-and-conquer approach to evolve the analog circuits. Corresponding to the two-layer GP, a new representation of circuit has been proposed here and it is more helpful to generate expectant circuit graphs. This algorithm can evolve the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this algorithm is efficient.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA.

A new circuit representation method for Analog circuit design automation

The Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a new circuit representation method based on a two-layer evolutionary scheme with genetic programming (TLGP), which uses a divide-and-conquer approach to evolve the analog circuits. This representation has the desirable property which is more helpful to generate expectant circuit graphs. And it is capable of generating various kinds of circuits by evolving the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this method is efficient.

X-ray Image Segmentation by Attribute Relational Graph Matching

This paper deals with the automatic threat detection for accompanied baggage based on multi- energy X-ray imagery, we propose a structural segmentation method based on ARG matching. The proposed segmentation algorithms are a series of graph-matching algorithms based on models under a kind of similarity measure, fuzzy similarity distance. The results show a good average integrity of objects segmented from experimental images.

Structural X-ray Image Segmentation for Threat Detection by Attribute Relational Graph Matching

This paper addresses part of the problem dealing with the automatic threat detection for accompanied baggage based on multi-energy X-ray imagery for station security. Segmentation is the first significant stage to extract interested objects in the images for detailed analysis and recognition at following stages. In order to obtain the integrated objects for subsequent analysis and recognition, we propose a structural segmentation method based on ARG matching. The proposed segmentation algorithms are a series of graph-matching algorithms based on models under a kind of similarity measure fuzzy similarity distance (FSD) that represents the similarity of the attributed relation between the vertex neighborhood and a certain model. Finally, the number of layer attribute for each region is obtained, and the integrated objects can be extracted using relational attributes and space information. The results show a good average integrity of objects segmented from experimental images

A differential evolution box-covering algorithm for fractal dimension on complex networks

The fractality property are discovered on complex networks through renormalization procedure, which is implemented by box-covering method. The unsolved problem of box-covering method is finding the minimum number of boxes to cover the whole network. Here, we introduce a differential evolution box-covering algorithm based on greedy graph coloring approach. We apply our algorithm on some benchmark networks with different structures, such as a E.coli metabolic network, which has low clustering coefficient and high modularity; a Clustered scale-free network, which has high clustering coefficient and low modularity; and some community networks (the Politics books network, the Dolphins network, and the American football games network), which have high clustering coefficient. Experimental results show that our algorithm can get better results than state of art algorithms in most cases, especially has significant improvement in clustered community networks.

Using selfish gene theory to construct mutual information and entropy based clusters for bivariate optimizations

This paper proposes a new approach named SGMIEC in the field of estimation of distribution algorithm (EDA). While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the selfish gene theory (SG) is deployed in this approach and a mutual information and entropy based cluster (MIEC) model with an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population. Experimental results on several benchmark problems demonstrate that, compared with BMDA, COMIT and MIMIC, SGMIEC often performs better in convergent reliability, convergent velocity and convergent process.

A new Evolutionary Algorithm based on quantum statistical mechanics

A new evolutionary algorithm based on quantum statistical mechanics (QSEA) is raised in this paper. In the algorithm, the whole evolutionary system is treated as a quantum statistical system, where quantum coding is adopted to express chromosomes, and superposition of quantum bits is used to simulate the linear superposition state of the system. Quantum system entropy and statistical energy have been defined by analogy with corresponding concepts in quantum statistical mechanics. And the competition between quantum statistical energy and entropy of the system is used to simulate the conflict between dasiaselection pressurepsila and dasiadiversity of populationpsila, which helps the algorithm to keep a delicate balance between these two issues, and obtain optimal solution rapidly. Numerical experiments show that this new algorithm has high efficiency and strong ability to get global optimal solution.