Multimedia Detection and Processing of Remote-Sensing Image of Small Target Combined with Variable Neighborhood Search Algorithm

The development of scientific satellites has made it a reality for people to view the Earth from the sky. However, due to the resolution of the image obtained, the effective and accurate interpretation of remote-sensing images has always been one of the goals pursued by the industry. In this paper, we merge the variable neighborhood search algorithm, reduce the accuracy of remote-sensing images, clean the invalid information of the data, use unsupervised classification methods to quickly locate small targets, use it as verification information, compare and select the image data through sample information, distinguish the background and target results, and get stable detection results. Practice shows that this method can effectively detect small targets in remote-sensing images.

A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem

The two-dimensional heterogeneous vector bin packing problem (2DHet-VBPP) consists of packing the set of items into the set of various type bins, respecting their two resource limits. The problem is to minimize the total cost of all bins. The problem, known to be NP-hard, can be formulated as a pure integer linear program, but optimal solutions can be obtained by the CPLEX Optimizer engine only for small instances. This paper proposes a metaheuristic approach to the 2DHet-VBPP, based on Reduced variable neighborhood search (RVNS). All RVNS elements are adapted to the considered problem and many procedures are designed to improve efficiency of the method. As the Two-dimensional Homogeneous-VBPP (2DHom-VBPP) is more often treated, we considered also a special version of the RVNS algorithm to solve the 2DHom-VBPP. The results obtained and compared to both CPLEX results and results on benchmark instances from literature, justify the use of the RVNS algorithm to solve large instances of these optimization problems.

A Colored Traveling Salesman Problem with Varying City Colors

A colored traveling salesman problem (CTSP) is a path optimization problem in which colors are used to characterize diverse matching relationship between cities and salesmen. Namely, each salesman has a single color while every city has one to multiple salesmen’s colors, thus allowing salesmen to visit exactly once the cities of their colors. It is noteworthy that cities’ accessibilities to salesmen may change over time, which usually takes place in the multiwarehouse distribution of online retailers. This work presents a new CTSP with dynamically varying city colors for describing and modeling some scheduling problems with variable city accessibilities. The problem is more complicated than the previously proposed CTSP with varying edge weights. In particular, the solution feasibility changes as the cities change their colors, that is, a feasible original solution path may become no longer feasible after city colors change. A variable neighborhood search (VNS) algorithm is presented to solve the new problem. Specifically, a dynamic environment simulator with an adjustable frequency and amplitude is designed to mimic such color changes. Then, direct-route encoding, greedy initialization, and appropriate population immigrant are proposed to form an enhanced VNS, and then its performance is evaluated. The results of extensive experiments show that the proposed VNS can quickly track the environmental changes and effectively resolve the problem.