Liangjun Ke

PLBP: An effective local binary patterns texture descriptor with pyramid representation

Local binary pattern (LBP) is an effective texture descriptor which has successful applications in texture classification and face recognition. Many extensions are made for conventional LBP descriptors. One of the extensions is dominant local binary patterns which aim at extracting the dominant local structures in texture images. The second extension is representing LBP descriptors in Gabor transform domain (LGBP). The third extension is multi-resolution LBP (MLBP). Another extension is dynamic LBP for video texture extraction. In this paper, we extend the conventional local binary pattern to pyramid transform domain (PLBP). By cascading the LBP information of hierarchical spatial pyramids, PLBP descriptors take texture resolution variations into account. PLBP descriptors show their effectiveness for texture representation. Comprehensive comparisons are made for LBP, MLBP, LGBP, and PLBP. Performances of no sampling, partial sampling and spatial pyramid sampling approaches for the construction of PLBP texture descriptors are compared. The influences of pyramid generation approaches, and pyramid levels to PLBP based image categorization performances are discussed. Compared to the existing multi-resolution LBP descriptors, PLBP is with satisfactory performances and with low computational costs.

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.

Ants can solve the team orienteering problem

The team orienteering problem (TOP) involves finding a set of paths from the starting point to the ending point such that the total collected reward received from visiting a subset of locations is maximized and the length of each path is restricted by a pre-specified limit. In this paper, an ant colony optimization (ACO) approach is proposed for the team orienteering problem. Four methods, i.e., the sequential, deterministic-concurrent and random-concurrent and simultaneous methods, are proposed to construct candidate solutions in the framework of ACO. We compare these methods according to the results obtained on well-known problems from the literature. Finally, we compare the algorithm with several existing algorithms. The results show that our algorithm is promising.

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.

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.

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.

Fireworks algorithm for the multi-satellite control resource scheduling problem

In this study, fireworks algorithm (FWA) for the multi-satellite control resource scheduling problem (MSCRSP) is presented. FWA is a meta-heuristic method and widely used in continuous problems while MSCRSP is a constrained and large scale combinatorial problem. The key points of FWA are to define a suitable neighborhood structure for launching the local search procedure and to find a metric for quantifying the disparity between solutions. Three kinds of neighborhood structures are presented and the best fitted one is picked. Due to the speciality of this problem, each solution is transformed into a binary vector, and Hamming distance is adopted for defining disparity metric. The experimental results demonstrate the proposed FWA is more competitive than those commonly used methods.

A multiobjective ACO algorithm for rough feature selection

Rough set theory has been widely applied to feature selection. In this paper, a multi-objective ant colony optimization algorithm is proposed for rough feature selection. This algorithm evaluates the constructed solutions on the basis of Pareto dominance. Moreover, it only uses the non-dominated solutions to add pheromone so as to reinforce the exploitation and adopts crowding comparison operator to maintain the diversity of the constructed solutions. In addition, it avoids premature convergence by imposing limits on pheromone values. Numerical experiments are carried out on gene expression datasets. Compared with a modified non-dominated sorting genetic algorithm, our algorithm can provide competitive solutions efficiently for rough feature selection.

A fast and efficient ant colony optimization approach for the set covering problem

In this paper, we present an ant colony optimization (ACO) approach to solve the set covering problem. A constraint-oriented solution construction method is proposed. The main difference between it and the existing method is that, while adding a column to the current partial solution, it randomly selects an uncovered row and only considers the columns covering the row, but not all the unselected columns as candidate solution components. This decreases the number of candidate solution components and therefore accelerates the run speed of the algorithm. Moreover, a simple but effective local search procedure, which aims at eliminating redundant columns and replacing some columns with more effective ones, is developed to improve the quality of solutions constructed by ants while keeping their feasibility. The proposed algorithm has been tested on a number of benchmark instances. Computational results indicate that it is capable of producing high quality solutions and performs better than the existing ACO-based algorithms.

A GA-ACO hybrid algorithm for the multi-UAV mission planning problem

Multi-UAV mission planning is a combinational optimization problem, that aims at planning a set of paths for UAVs to visit targets in order to collect the maximum surveillance benefits, while satisfying some constraints. In this paper, a genetic algorithm and ant colony optimization hybrid algorithm is proposed to solve the multi-UAV mission planning. The basic idea of the proposed hybrid algorithm is replacing the bad individuals of the GAs population by new individuals constructed by ant colony algorithm. Also, an efficient recombination operator called path relinking is used for mating. A population partition strategy is adopted for improving the evolving efficiency. Experimental results suggested that the proposed hybrid algorithm can solve the test instances effectively in a reasonable time. The comparison study with several existing algorithms shows that the proposed algorithm is competitive and promising.