Shengyong Chen

Markov Models for Image Labeling

Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.

Traffic Dynamics on Complex Networks: A Survey

Traffic dynamics on complex networks are intriguing in recent years due to their practical implications in real communication networks. In this survey, we give a brief review of studies on traffic routing dynamics on complex networks. Strategies for improving transport efficiency, including designing efficient routing strategies and making appropriate adjustments to the underlying network structure, are introduced in this survey. Finally, a few open problems are discussed in this survey.

Multiobjective fireworks optimization for variable-rate fertilization in oil crop production

Abstract Variable-rate fertilization (VRF) decision is a key aspect of prescription generation in precision agriculture, which typically involves multiple criteria and objectives. This paper presents a multiobjective optimization problem model for oil crop fertilization, which takes into consideration not only crop yield and quality but also energy consumption and environmental effects. For efficiently solving the problem, we propose a hybrid multiobjective fireworks optimization algorithm (MOFOA) that evolves a set of solutions to the Pareto optimal front by mimicking the explosion of fireworks. In particular, it uses the concept of Pareto dominance for individual evaluation and selection, and combines differential evolution (DE) operators to increase information sharing among the individuals. The experimental tests and real-world applications in oil crop production in east China demonstrate the effectiveness and practicality of the algorithm.

Population Classification in Fire Evacuation: A Multiobjective Particle Swarm Optimization Approach

In an emergency evacuation operation, accurate classification of the evacuee population can provide important information to support the responders in decision making; and therefore, makes a great contribution in protecting the population from potential harm. However, real-world data of fire evacuation is often noisy, incomplete, and inconsistent, and the response time of population classification is very limited. In this paper, we propose an effective multiobjective particle swarm optimization method for population classification in fire evacuation operations, which simultaneously optimizes the precision and recall measures of the classification rules. We design an effective approach for encoding classification rules, and use a comprehensive learning strategy for evolving particles and maintaining diversity of the swarm. Comparative experiments show that the proposed method performs better than some state-of-the-art methods for classification rule mining, especially on the real-world fire evacuation dataset. This paper also reports a successful application of our method in a real-world fire evacuation operation that recently occurred in China. The method can be easily extended to many other multiobjective rule mining problems.

A hybrid fireworks optimization method with differential evolution operators

Fireworks algorithm (FA) is a relatively new swarm-based metaheuristic for global optimization. The algorithm is inspired by the phenomenon of fireworks display and has a promising performance on a number of benchmark functions. However, in the sense of swarm intelligence, the individuals including fireworks and sparks are not well-informed by the whole swarm. In this paper we develop an improved version of the FA by combining with differential evolution (DE) operators: mutation, crossover, and selection. At each iteration of the algorithm, most of the newly generated solutions are updated under the guidance of two different vectors that are randomly selected from highly ranked solutions, which increases the information sharing among the individual solutions to a great extent. Experimental results show that the DE operators can improve diversity and avoid prematurity effectively, and the hybrid method outperforms both the FA and the DE on the selected benchmark functions.

Evolutionary optimization for disaster relief operations

Graphical abstractDisplay Omitted HighlightsWe provide an overview of evolutionary algorithms for disaster relief operations.We show major strengths and shortcomings of the state-of-the-arts.We discuss potential directions for future research. Effective planning and scheduling of relief operations play a key role in saving lives and reducing damage in disasters. These emergency operations involve a variety of challenging optimization problems, for which evolutionary computation methods are well suited. In this paper we survey the research advances in evolutionary algorithms (EAs) applied to disaster relief operations. The operational problems are classified into five typical categories, and representative works on EAs for solving the problems are summarized, in order to give readers a general overview of the state-of-the-arts and facilitate them to find suitable methods in practical applications. Several state-of-art methods are compared on a set of real-world emergency transportation problem instances, and some lessons are drawn from the experimental analysis. Finally, the strengths, limitations and future directions in the area are discussed.

Letters: Leader-following consensus of discrete-time multi-agent systems with observer-based protocols

This paper investigates the leader-following consensus problem of discrete-time multi-agent systems. The dynamics of the leader and all following agents adopt the same general form of a linear model that can be of any order. The interconnection topology among the agents is assumed to be switching and undirected. To track the active leader, two kinds of distributed observer-based consensus protocols are proposed for each following agent, whose distributed observers are used to estimate the leaders state and the tracking error based on the relative outputs of neighboring agents, respectively. In light of the modified discrete-time algebraic Riccati equality and Lyapunov method, we prove that the discrete-time leader-following consensus problem can be solved by proposing the distributed observer-based consensus protocol under switching topologies. Finally, a numerical example is given to illustrate the obtained result.

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Cooperative particle swarm optimization for multiobjective transportation planning

The paper presents a multiobjective optimization problem that considers distributing multiple kinds of products from multiple sources to multiple targets. The problem is of high complexity and is difficult to solve using classical heuristics. We propose for the problem a hierarchical cooperative optimization approach that decomposes the problem into low-dimensional subcomponents, and applies Pareto-based particle swarm optimization (PSO) method to the main problem and the subproblems alternately. In particular, our approach uses multiple sub-swarms to evolve the sub-solutions concurrently, controls the detrimental effect of variable correlation by reducing the subproblem objectives, and brings together the results of the sub-swarms to construct effective solutions of the original problem. Computational experiment demonstrates that the proposed algorithm is robust and scalable, and outperforms some state-of-the-art constrained multiobjective optimization algorithms on a set of test problems.

Emergency railway wagon scheduling by hybrid biogeography-based optimization

Railway transportation plays an important role in many disaster relief and other emergency supply chains. Based on the analysis of several recent disaster rescue operations in China, the paper proposes a mathematical model for emergency railway wagon scheduling, which considers multiple target stations requiring relief supplies, source stations for providing supplies, and central stations for allocating railway wagons. Under the emergency environment, the aim of the problem is to minimize the weighted time for delivering all the required supplies to the targets. For efficiently solving the problem, we develop a new hybrid biogeography-based optimization (BBO) algorithm, which uses a local ring topology of population to avoid premature convergence, includes the differential evolution (DE) mutation operator to perform effective exploration, and takes some problem-specific mechanisms for fine-tuning the search process and handling the constraints. Computational experiments show that our algorithm is robust and scalable, and outperforms some state-of-the-art heuristic algorithms on a set of problem instances.