As a novel meta-heuristic algorithm, the Whale Optimization Algorithm (WOA) has well performance in solving optimization problems. However, WOA usually tends to trap in local optimal and it suffers slow convergence speed for large-scale and high-dimension optimization problems. A modified whale optimization algorithm with single-dimensional swimming (abbreviated as SWWOA) is proposed in order to overcome the shortcoming. First, tent map is applied to generate the initialize population for maximize search ability. Second, quasi-opposition learning is adopted after every iteration for further improving the search ability. Third, a novel nonlinearly control parameter factor that is based on logarithm function is presented in order to balance exploration and exploitation. Additionally, the last, single-dimensional swimming is proposed in order to replace the prey behaviour in standard WOA for tuning. The simulation experiments were conducted on 20 well-known benchmark functions. The results show that the proposed SWWOA has better performance in solution precision and higher convergence speed than the comparison methods.

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms.

No abstract

No abstract

Zero Shot Learning (ZSL) aims to solve the classification problem with no training sample, and it is realized by transferring knowledge from source classes to target classes through the semantic embeddings bridging. Generalized ZSL (GZSL) enlarges the search scope of ZSL from only the seen classes to all classes. A large number of methods are proposed for these two settings, and achieve competing performance. However, most of them still suffer from the domain shift problem due to the existence of the domain gap between the seen classes and unseen classes. In this paper, we propose a novel method to learn discriminative features with visual-semantic alignment for GZSL. We define a latent space, where the visual features and semantic attributes are aligned, and assume that each prototype is the linear combination of others, where the coefficients are constrained to be the same in all three spaces. To make the latent space more discriminative, a linear discriminative analysis strategy is employed to learn the projection matrix from visual space to latent space. Five popular datasets are exploited to evaluate the proposed method, and the results demonstrate the superiority of our approach compared with the state-of-the-art methods. Beside, extensive ablation studies also show the effectiveness of each module in our method.