Discriminative Codebook Hashing for Supervised Video Retrieval

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback–Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.

Improved Dragonfly Algorithm with Neighbourhood Structures

Dragonfly algorithm is a recently proposed optimization algorithm inspired on the static and dynamic swarming behaviour of dragonflies. Because of its simplicity and effectiveness, DA has received interest of specialists from various fields. Premature convergence and local optima is an issue in Dragonfly Algorithm. Improved Dragonfly Algorithm with Neighbourhood Structures (IDANS) is proposed to overcome this drawback. Dragonfly Algorithm with Neighborhood structures utilizes candidate solutions in an iterative and intuitive process to discover promising areas in a search space. IDANS is then initialized with best value of dragonfly algorithm to further explore the search space. In order to improve the efficiency of IDANS, Neighbourhood structures such as Euclidean, Manhattan and Chebyshev are chosen to implement these structures on IDANS to obtain best results. The proposed method avoids local optima to achieve global optimal solutions. The Efficiency of the IDANS is validated by testing on benchmark functions and classical engineering problem called Gear train design problem. A comparative performance analysis between IDANS and other powerful optimization algorithms have been carried out and the results shows that IDANS gives better performance than Dragonfly algorithm. Moreover it gives competitive results in terms of convergence and accuracy when compared with other algorithms in the literature.

Elastic Embedding through Graph Convolution-based Regression for Semi-supervised Classification

This article introduces a scheme for semi-supervised learning by estimating a flexible non-linear data representation that exploits Spectral Graph Convolutions structure. Structured data are exploited in order to determine non-linear and linear models. The introduced scheme takes advantage of data-driven graphs at two levels. First, it incorporates manifold smoothness that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are derived from the data and their associated graph. The proposed semi-supervised embedding can tackle the problem of over-fitting on neighborhood structures for image data. The proposed Graph Convolution-based Semi-supervised Embedding paves the way to new theoretical and application perspectives related to the non-linear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with some state-of-the-art semi-supervised approaches. This empirical evaluation shows the effectiveness of the proposed embedding scheme.

The Bike-Sharing Rebalancing Problem Considering Multi-Energy Mixed Fleets and Traffic Restrictions

Nowadays, as a low-carbon and sustainable transport mode bike-sharing systems are increasingly popular all over the world, as they can reduce road congestion and decrease greenhouse gas emissions. Aiming at the problem of the mismatch of bike supply and user demand, the operators have to transfer bikes from surplus stations to deficiency stations to redistribute them among stations by vehicles. In this paper, we consider a mixed fleet of electric vehicles and internal combustion vehicles as well as the traffic restrictions to the traditional vehicles in some metropolises. The mixed integer programming model is firstly established with the objective of minimizing the total rebalancing cost of the mixed fleet. Then, a simulated annealing algorithm enhanced with variable neighborhood structures is designed and applied to a set of randomly generated test instances. The computational results and sensitivity analysis indicate that the proposed algorithm can effectively reduce the total cost of rebalancing.

Hybrid Cuckoo Search for the Capacitated Vehicle Routing Problem

Having the best solution for Vehicle Routing Problem (VRP) is still in demand. Beside, Cuckoo Search (CS) is a popular metaheuristic based on the reproductive strategy of the Cuckoo species and has been successfully applied in various optimizations, including Capacitated Vehicle Routing Problem (CVRP). Although CS and hybrid CS have been proposed for CVRP, the performance of CS is far from the state-of-art. Therefore, this study proposes a hybrid CS with Simulated Annealing (SA) algorithm for the CVRP, consisting of three improvements—the investigation of 12 neighborhood structures, three selections strategy and hybrid it with SA. The experiment was conducted using 16 instances of the Augerat benchmark dataset. The results show that 6 out of 12 neighborhood structures were the best and the disruptive selection strategy is the best strategy. The experiments’ results showed that the proposed method could find optimal and near-optimal solutions compared with state-of-the-art algorithms.

Discrete African Buffalo Optimization Algorithm for the Low-carbon Flexible Job Shop Scheduling Problem

In the area of production scheduling, some traditional indicators are always treated as the optimization objectives such as makespan, earliness/tardiness and workload, and so on. However, with the increasing amount of energy consumption, the low-carbon scheduling problem has gained more and more attention from scholars and engineers. In this paper, a low-carbon flexible job shop scheduling problem (LFJSP) is studied to minimize the earliness/tardiness cost and the energy consumption cost. In this paper, a low-carbon flexible job shop scheduling. Due to the NP-hard nature of the problem, a swarm-based intelligence algorithm, named discrete African buffalo optimization (DABO), is developed to deal with the problem under study effectively. The original ABO was proposed for continuous problems, but the problem is a discrete scheduling problem. Therefore, some individual updating methods are proposed to ensure the algorithm works in a discrete search domain. Then, some neighborhood structures are designed in terms of the characteristics of the problem. A local search procedure is presented based on some neighborhood structures and embedded into the algorithm to enhance its searchability. In addition, an aging-based population re-initialization method is proposed to enhance the population diversity and avoid trapping into the local optima. Finally, several experimental simulations have been carried out to test the effectiveness of the DABO. The comparison results demonstrate the promising advantages of the DABO for the considered LFJSP.

Optimizing Computer Networks Communication with the Band Collocation Problem: A Variable Neighborhood Search Approach

The Band Collocation Problem appears in the context of problems for optimizing telecommunication networks with the aim of solving some concerns related to the original Bandpass Problem and to present a more realistic approximation to be solved. This problem is interesting to optimize the cost of networks with several devices connected, such as networks with several embedded systems transmitting information among them. Despite the real-world applications of this problem, it has been mostly ignored from a heuristic point of view, with the Simulated Annealing algorithm being the best method found in the literature. In this work, three Variable Neighborhood Search (VNS) variants are presented, as well as three neighborhood structures and a novel optimization based on Least Recently Used cache, which allows the algorithm to perform an efficient evaluation of the objective function. The extensive experimental results section shows the superiority of the proposal with respect to the best previous method found in the state-of-the-art, emerging VNS as the most competitive method to deal with the Band Collocation Problem.