We investigate the distributed stochastic optimization by nodes over the uncertain communication topologies to cooperatively minimize a sum of strongly convex local cost functions. The communication topologies are des...
详细信息
Our study focuses on the development of new Estimation of Distribution algorithms (EDAs) with neuro-evolution for pseudo-Boolean optimization problems. We define a strategy for updating the frequency vector at each ge...
详细信息
This comprehensive review explores a range of optimization approaches for Combined Economic Emission Dispatch (CEED), covering conventional, non-conventional, and hybrid techniques. CEED is critical in minimizing econ...
详细信息
This comprehensive review explores a range of optimization approaches for Combined Economic Emission Dispatch (CEED), covering conventional, non-conventional, and hybrid techniques. CEED is critical in minimizing economic costs and emissions while ensuring power system reliability. Traditional methods focus on cost minimization but overlook environmental considerations. optimization techniques address this gap by simultaneously optimizing economic and environmental objectives. Hybrid techniques, combining multiple algorithms or integrating renewable energy, further enhance CEED performance. The review evaluates these approaches' strengths and limitations, considering factors like computational efficiency and solution accuracy. Over the past few decades, a great deal of study has been done on the use of renewable energy (RE) as an alternative source in power generation systems. As a result, the power dispatch problem currently uses the Combined Economic Emission Dispatch (CEED) of thermal and renewable energy resources. It discusses the potential of hybrid techniques and take in consideration renewable energy integration in achieving cost savings and emission reductions, highlighting areas for future research.
Mean-field spin glasses are families of random energy functions (Hamiltonians) on high-dimensional product spaces. In this paper, we consider the case of Ising mixed p-spin models,;namely, Hamiltonians H-N : Sigma(N) ...
详细信息
Mean-field spin glasses are families of random energy functions (Hamiltonians) on high-dimensional product spaces. In this paper, we consider the case of Ising mixed p-spin models,;namely, Hamiltonians H-N : Sigma(N) -> R on the Hamming hypercube Sigma(N) = [+/- 1](N), which are defined by the property that {H-N(sigma)}(sigma is an element of Sigma N) is a centered Gaussian process with covariance E{H-N(sigma(1)) H-N(sigma(2))} depending only on the scalar product (sigma(1), sigma(2)). The asymptotic value of the optimum max(sigma is an element of Sigma N) H-N (sigma) was characterized in terms of a variational principle known as the Parisi formula, first proved by Talagrand and, in a more general setting, by Panchenko. The structure of superlevel sets is extremely rich and has been studied by a number of authors. Here, we ask whether a near optimal configuration sigma can be computed in polynomial time. We develop a message passing algorithm whose complexity per-iteration is of the same order as the complexity of evaluating the gradient of H-N, and characterize the typical energy value it achieves. When the p-spin model H-N satisfies a certain no-overlap gap assumption, for any epsilon > 0, the algorithm outputs sigma is an element of Sigma(N) such that H-N (sigma) >= (1 - epsilon) max(sigma') H-N (sigma'), with high probability. The number of iterations is bounded in N and depends uniquely on epsilon. More generally, regardless of whether the no-overlap gap assumption holds, the energy achieved is given by an extended variational principle, which generalizes the Parisi formula.
作者：
Amala, K.J.Rajeswari, D.School of Computing
College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India
Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framew...
详细信息
The analysis of dynamic systems is essential in the design of both classical and modern controllers, especially in situations where obtaining accurate model parameters is complex. This difficulty stems from the uncert...
详细信息
In this paper, we consider a generalized ranking and selection problem, where each system’s performance depends on a continuous decision variable necessitating optimization. We focus on a fixed confidence formulation...
详细信息
In the paper, an fusion of optimized A∗ algorithm and Dynamic Window Approach (DWA) algorithm to pathfinding is presented, combining the strengths of both the A∗ and DWA algorithms. The innovation of the fusion algori...
详细信息
We propose an exact method that finds a minimum complete Pareto front of the biobjective minimum length minimum risk spanning trees problem. The method consists in two algorithms. The first algorithm finds a single mi...
详细信息
We study variants of the Optimal Refugee Resettlement problem where a set F of refugee families need to be allocated to a set P of possible places of resettlement in a feasible and optimal way. Feasibility issues emer...
详细信息
暂无评论