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With the advent of sequencing technology, numerous gene expression data are generated. Identifying differentially expressed genes play an important role in the gene therapy of cancer patients. As an useful mathematical tool, nonnegative matrix factorization (NMF) has been successfully used for identifying differentially expressed genes. In this paper, a novel method named robust graph regularized...
FusionGAN is a novel genre fusion framework for music generation that integrates the strengths of generative adversarial networks and dual learning. In particular, the proposed method offers a dual learning extension that can effectively integrate the styles of the given domains. To efficiently quantify the difference among diverse domains and avoid the vanishing gradient issue, FusionGAN provides...
Maintaining the balance between convergence and diversity plays a vital role in multi-objective evolutionary algorithms (MOEAs). However, most MOEAs cannot reach a satisfying balance, especially when solving problems having complicated pareto optimal sets. In this paper, we present a modified cooperative co-evolution approach for achieving better convergence and diversity simultaneously (namely DPP2)...
We consider a constrained multi-agent optimization problem where the bit rate of communication in the network is limited. This problem arises in a network with time-varying connectivity where all the agents try to minimize a sum of nonsmooth but Lipschitz continuous functions, and the estimates of each agent are restricted to lie in the same convex set. We design a uniform quantizer and present a...
Wolf pack algorithm is one of the group intelligence algorithms, which has advantages in convergence rate and objective function solving precision. But there still exists deficiency: slow convergence speed, easy to fall into the local extremum, the searching precision is not ideal and so on. In this paper, The Tent chaotic mapping strategy is used to make the population distribution even more uniform...
the optimization algorithm plays an important role in solving the complex problems, and many complex problems can be modeled as a combinatorial optimization problem. The multi-dimensional knapsack problem is a kind of typical combinatorial optimization problem. The pollination algorithm is a kind of natural heuristic algorithm proposed in recent years, which has the characteristics of few parameter...
Distribution network reconstruction has caused widespread concern of the community. Firstly, the method of calculating the power flow of the ant colony algorithm is analyzed, and puts forward the hybrid modified ant colony algorithm of distribution network reconfiguration method, using simulated annealing algorithm to generate the ant pheromone distribution, with increasing attachment branch impedance...
The traditional iterative closest point (ICP) algorithm could register two points sets well, but it is easily affected by local dissimilar. To deal with this problem, this paper proposes an isotropic scaling ICP algorithm with corner point constraint. First, an objective function is proposed under the guidance of the corner points, as the corner points can preserve the similar of the whole shapes...
It is known that the Gradient descent bit flipping (GDBF) algorithm is an effective hard-decision decoding algorithm for low-density parity-check (LDPC) codes. However, trapping in a local maximum limits its error-rate performance. This paper presents a modified GDBF scheme that can mitigate the trapping problem and hence can improve the error-rate performance. Compared to the conventional GDBF algorithm,...
Dual methods can handle easily complicated constraints in convex problems, but they have typically slow (sublinear) convergence rate in an average primal point, even when the original problem has smooth strongly convex objective function. Primal projected gradient-based methods achieve linear convergence for constrained, smooth and strongly convex optimization, but it is difficult to implement them,...
The dynamic characteristics of a hydraulic turbine governing system is determined by the parameters of the hydraulic turbine governor. There are several drawbacks of the conventional particle swarm algorithm in parameter optimization, such as low speed of convergence, low accuracy and being inclined to result in partial optimization during the process of optimization. This paper introduced concave...
Multiview canonical correlation analysis (MCCA) is an effective tool for analyzing the relationships among group- aligned multidimensional samples, which has been applied to the fields of pattern recognition and computer vision. In MCCA, its first-stage canonical variables are solved by a multivariate eigenvalue problem that can be computed by Horst method. However, how to use the algorithm for effectively...
The objective of the paper is to implement alternative direction method of multipliers (ADMM) to solve a nonconvex alternating current optimal power flow (AC OPF) problem. There is no guarantee of convergence for ADMM when it is applied to a nonconvex optimization problem. In this article, we not only present the procedure of consensus ADMM implementation on an AC OPF problem for IEEE 14-bus system...
Logistics distribution center location problem is a hot topic nowadays. We choose a medium-sized city group to study in this paper. The improved artificial immune algorithm based on similarity vector distance selection is used for the logistics distribution center location problem. The algorithm uses the threshold value to restrict concentration calculation of two antibodies from two aspects which...
Recently, the integration of distributed generation (DG) units to distribution networks has grown significantly. This integration provides an opportunity to control the power flow, resulting in the optimal power flow (OPF) at the distribution level. OPF can reduce system losses and decrease the DG generation costs. Additionally, it can improve the voltage profile. Applying OPF to distribution networks...
Motivated by broad applications in various fields of engineering, we study network resource allocation problems where the goal is to optimally allocate a fixed portion of resources over a network of nodes. In these problems, due to the large scale of the network and complicated interconnections between nodes, any solution must be implemented in parallel and based only on local data resulting in a...
In this paper, a subgradient method is developed to solve the system of (nonsmooth) equations. First, the system of (nonsmooth) equations is transformed into a nonsmooth optimization problem with zero minimal objective function value. Then, a subgradient method is applied to solve the nonsmooth optimization problem. During the processes, the pre-known optimal objective function value is adopted to...
Path planning based on heuristic optimization method is developed to simplify the path planning issues into optimization problems. Particle Swarm Optimization (PSO) is one of the heuristic optimization methods often used because of its simplicity, easy to implement and has few parameters to set. However, the basic PSO algorithm has difficulties balancing exploration and exploitation, and suffer from...
Comprehensive learning particle swarm optimization (CLPSO) algorithm has a good performance in overcoming premature convergence and avoiding getting stuck in local minima, which are shortcomings in particle swarm optimization. It can solve complex, multi-modal of single-objective problems, but it has not such performance in handling multi-objective optimization problems because of the difficulty of...
Many modern computer vision and machine learning applications rely on solving difficult optimization problems that involve non-differentiable objective functions and constraints. The alternating direction method of multipliers (ADMM) is a widely used approach to solve such problems. Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly...
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