Zuren Feng

An efficient ant colony optimization approach to attribute reduction in rough set theory

Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we introduce a new approach based on ant colony optimization (ACO) for attribute reduction. To verify the proposed algorithm, numerical experiments are carried out on thirteen small or medium-sized datasets and three gene expression datasets. The results demonstrate that this algorithm can provide competitive solutions efficiently.

New ideas for applying ant colony optimization to the set covering problem

The set covering problem (SCP) is a well known NP-hard problem with many practical applications. In this research, a new approach based on ant colony optimization (ACO) is proposed to solve the SCP. The main differences between it and the existing ACO-based approaches lie in three aspects. First, it adopts a novel method, called single-row-oriented method, to construct solutions. When choosing a new column, it first randomly selects an uncovered row and only considers the columns covering this row, rather than all the unselected columns as candidate solution components. Second, a kind of dynamic heuristic information is used in this approach. It takes into account Lagrangian dual information associated with currently uncovered rows. Finally, a simple local search procedure is developed to improve solutions constructed by ants while keeping their feasibility. The proposed algorithm has been tested on a number of benchmark instances. Computational results show that it is able to produce competitive solutions in comparison with other metaheuristics.

Indirect Iterative Learning Control for a Discrete Visual Servo Without a Camera-Robot Model

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

An ant colony optimization approach for the multidimensional knapsack problem

Ant colony optimization is a metaheuristic that has been applied to a variety of combinatorial optimization problems. In this paper, an ant colony optimization approach is proposed to deal with the multidimensional knapsack problem. It is an extension of Max Min Ant System which imposes lower and upper trail limits on pheromone values to avoid stagnation. In order to choose the lower trail limit, we provide a new method which takes into account the influence of heuristic information. Furthermore, a local search procedure is proposed to improve the solutions constructed by ants. Computational experiments on benchmark problems are carried out. The results show that the proposed algorithm can compete efficiently with other promising approaches to the problem.

Multi-satellite control resource scheduling based on ant colony optimization

The multi-satellite control resource scheduling problem (MSCRSP) is a kind of large-scale combinatorial optimization problem. As the solution space of the problem is sparse, the optimization process is very complicated. Ant colony optimization as one of heuristic method is wildly used by other researchers to solve many practical problems. An algorithm of multi-satellite control resource scheduling problem based on ant colony optimization (MSCRSP-ACO) is presented in this paper. The main idea of MSCRSP-ACO is that pheromone trail update by two stages to avoid algorithm trapping into local optima. The main procedures of this algorithm contain three processes. Firstly, the data get by satellite control center should be preprocessed according to visible arcs. Secondly, aiming to minimize the working burden as optimization objective, the optimization model of MSCRSP, called complex independent set model (CISM), is developed based on visible arcs and working periods. Ant colony algorithm can be used directly to solve CISM. Lastly, a novel ant colony algorithm, called MSCRSP-ACO, is applied to CISM. From the definition of pheromone and heuristic information to the updating strategy of pheromone is described detailed. The effect of parameters on the algorithm performance is also studied by experimental method. The experiment results demonstrate that the global exploration ability and solution quality of the MSCRSP-ACO is superior to existed algorithms such as genetic algorithm, iterative repair algorithm and max-min ant system.

A two-phase metaheuristic for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem, which aims to minimize the total arrival time at customers, is a relatively new variant of vehicle routing problem. It can be used to model many real-world applications, e.g., the important application arisen from the humanitarian aid after a natural disaster. In this paper, an approach, called two-phase metaheuristic, is proposed to deal with this problem. This algorithm starts from a solution. At each iteration, two interdependent phases use different perturbation and local search operators for solution improvement. The effectiveness of the proposed algorithm is empirically investigated. The comparison results show that the proposed algorithm is promising. Moreover, for nine benchmark instances, the two-phase metaheuristic can find better solutions than those reported in the previous literature.

Ant colony optimization for image segmentation

It is found that the multistage decision algorithm for image segmentation with active contour model (ACM) is similar to ant colony optimization (ACO). By means of constructing solution space and heuristic information, a new algorithm based on ACM is proposed in the paper, which uses ACO to search for the best path in a constrained region. This algorithm that provides a new approach to obtain precise contour, is proved to be convergent with probability one, and will reach the best feasible boundary with minimum energy function value. Moreover, this algorithm can also be used to solve other revised ACM problems. The simulation results show that the proposed approach is more effective than the genetic algorithm in literature (Mishraa et al., 2003).

Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series

Reservoir computing methods have become popular; however, the nature of the dynamical reservoir (DR) is not thoroughly understood yet. We propose complex echo state network (CESN), the construction process of its DR is determined by five growth factors. The relationships between CESN connectivity structure and its performance are investigated when predicting nonlinear time series. We also introduce a quantifiable characteristic for the connectivity structure-the connectivity index, and a tool to measure the richness of reservoir states-the omega-complexity index. It is demonstrated from the experimental results that connectivity structure of the reservoirs has significant effect on theirs prediction performance, the omega-complexity index can be used as a performance predictor, and particular configuration of the growth factors and corresponding connectivity index can yield optimal performance.

A tabu-search algorithm for scheduling jobs with controllable processing times on a single machine to meet due-dates

In many real-world production systems, it is important to schedule jobs such that they could be processed and shipped with no delay. In this paper, we consider the problem of scheduling n jobs with arbitrary release dates and due dates on a single machine, where job-processing times can be controlled by the allocation of a common resource, and the operation is modeled by a non-linear convex resource consumption function. The objective is to obtain an optimal processing permutation as well as an optimal resource allocation, such that all the jobs can be finished no later than their due dates, and the total resource consumption can be minimized. The problem is strongly NP- hard. A two-layer-structured algorithm based on the tabu-search is presented. The computational result, compared with that from a branch and bound algorithm, shows the algorithm is capable of producing optimal or near optimal solution for large-sized problems in an acceptable computational time.

Two-machine no-wait flowshop scheduling with learning effect and convex resource-dependent processing times

Abstract Two-machine no-wait flowshop scheduling problems in which the processing time of a job is a function of its position in the sequence and its resource allocation are considered in the study. The primary objective is to find the optimal sequence of jobs and the optimal resource allocation separately. Here we propose two separate models: minimizing a cost function of makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function of makespan, total waiting time, total absolute differences in waiting times and total resource cost. Since each model is strongly NP-hard, we solve both models by breaking them down to two sub-problems, the optimal resource allocation problem for any job sequence and the optimal sequence problem with its optimal resource allocation. Specially, we transform the second sub-problem into the minimum of the bipartite graph optimal matching problem (NP-hard), and solve it by using the classic KM (Kuhn–Munkres) algorithm. The solutions of the two sub-problems demonstrate that the target problems remain polynomial solvable under the proposed model.