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New Pareto eigenvalue inclusion intervals for tensors are proposed to provide some checkable sufficient criteria for the copositivity of tensors, which are depended only on the negative and diagonal entries of a tensor. The relationships among ...

In this paper, exploiting the structure of tensors, the so called refined Geršhgorin disks for H-eigenvalues of tensors are introduced, which are always tighter than the classical Geršhgorin-type theorem for H-eigenvalues of tensors introduced by ...$\mathcal{}$$\mathcal{}$$\mathcal{}$$\mathcal{}{}^{\mathrm{}}\mathbf{}$$\mathcal{}$${\mathcal{}}^{}$$\mathbf{}{\mathbb{}}^{}$

The granular ball k-nearest neighbors (GBKNN) algorithm improves the efficiency and robustness of traditional k-nearest neighbors (KNN) by replacing point input with granular ball. However, in the generation process of granular balls by GBKNN, ...

Online streaming feature selection (OSFS), as an online learning manner to handle streaming features, is critical in addressing high‐dimensional data. In real big data‐related applications, the patterns and distributions of streaming features ...

In recent years, many improved multi-objective particle swarm optimization algorithms have been proposed to solve multi-objective optimization problems, but there are still some shortcomings. Therefore, in order to enhance the convergence and ...

To effectively improve the convergence and diversity of the multiobjective particle swarm optimization (MOPSO), we proposed a novel MOPSO based on ranking and cyclic distance (RCDMOPSO) that comprehensively considers the spatial target and ...

To improve convergence performance of the algorithm and prevent the algorithm from falling into local optimal location, we proposes a novel fuzzy multi-objective particle swarm optimization based on linear differential decline (LDDFMOPSO). In ...

The multiobjective optimization problems are a common problem in various fields in the real society. Therefore, solving the multiobjective optimization problems are one of the important problems studied by many researchers in recent years. From the ...

Although the principle of multi-objective particle swarm optimization is simple and the operability is strong, it is still prone to local convergence and the convergence accuracy is not high. In order to solve the above problems, we propose a multi-...

In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (...

The localization problem of unknown nodes in wireless sensor networks (WSN) has drawn increasing scholarly attention along together the popularity of meta-heuristic algorithms. To overcome the shortcomings of low accuracy that ...

The optimization problems are taking place at all times in actual lives. They are divided into single objective problems and multiobjective problems. Single objective optimization has only one objective function, while multiobjective optimization has ...

This paper proposes a novel binary particle swarm optimization algorithm (called IRBPSO) to address high-dimensional knapsack problems in dynamic environments (DKPs). The IRBPSO integrates an immune-based response strategy into the basic binary ...

This paper is concerned with the delay-dependent stability of systems with distributed time-varying delays. The novelty relies on the use of some new inequalities which are less conservative than some existing inequalities. A less conservative ...

For the visual navigation system of unmanned aerial vehicles (UAV), it is of importance to estimate the relative time delay between camera and inertial measurement unit (IMU) because the image data with undetermined delay cannot meet the synchronous ...

In this paper, a new mechanism of velocity update in multi-objective particle swarm optimization (VM-MOPSO) is proposed. The main goal of the method is to balance local exploration and global exploration of multi-objective particle swarm ...

To balance convergence and diversity of multi-objective particle swarm optimization, a new mechanism of position update in multi-objective particle swarm optimization (PM-MOPSO) is proposed in this paper. In PM-MOPSO, according to the relationship ...

This paper presents a Coevolutionary Structure-Redesigned-Based Bacteria Foraging Optimization CSRBFO based on the natural phenomenon that most living creatures tend to cooperate with each other so as to fulfill tasks more effectively. Aiming at ...

The interval-valued intuitionistic fuzzy set proposed by Atanassov is the extension of intuitionistic fuzzy set. It extends the membership degree and non-membership to interval values instead of a single value. So it contains more possible values and ...