A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem

• URL: http://arxiv.org/abs/2312.11527v2
• Date: Sat, 25 May 2024 13:43:18 GMT
• Title: A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem
• Authors: Hayet Dahmri, Salim Bouamama,
• Abstract summary: We propose a simulated algorithm based on a greedy for tackling a variant of the minimum connected dominating set problem. Experimental results compared to those obtained by a recent proposed research show the superiority of our approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Minimum connected dominating set problem is an NP-hard combinatorial optimization problem in graph theory. Finding connected dominating set is of high interest in various domains such as wireless sensor networks, optical networks, and systems biology. Its weighted variant named minimum weight connected dominating set is also useful in such applications. In this paper, we propose a simulated annealing algorithm based on a greedy heuristic for tackling a variant of the minimum connected dominating set problem and that by exploiting two objectives together namely the cardinality and the total weight of the connected dominating set. Experimental results compared to those obtained by a recent proposed research show the superiority of our approach.

Related papers

• Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks [57.24087627267086]
  We consider the task of minimizing the sum of convex functions stored in a decentralized manner across the nodes of a communication network. Lower bounds on the number of decentralized communications and (sub)gradient computations required to solve the problem have been established. We develop the first optimal algorithm that matches these lower bounds and offers substantially improved theoretical performance compared to the existing state of the art.
  arXiv  Detail & Related papers  (2024-05-28T10:28:45Z)
• Equivariant Deep Weight Space Alignment [54.65847470115314]
  We propose a novel framework aimed at learning to solve the weight alignment problem. We first prove that weight alignment adheres to two fundamental symmetries and then, propose a deep architecture that respects these symmetries.
  arXiv  Detail & Related papers  (2023-10-20T10:12:06Z)
• Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks [1.5469452301122175]
  A dominating set of a graph $mathcalG=(V, E) is a subset of vertices $SsubseteqmathcalV setminus S$ outside the dominating set. We propose a novel learning-based approach to compute solutions for the minimum dominating set problem using graph$ convolutional networks.
  arXiv  Detail & Related papers  (2023-06-06T06:22:42Z)
• Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems [16.803653908913514]
  We study the multi-objective minimum weight base problem, an abstraction of classical NP-hard problems such as the multi-objective minimum spanning tree problem. We prove some important properties of the convex hull of the non-dominated front, such as its approximation quality and an upper bound on the number of extreme points. We show that the MOEA/D algorithm, given an appropriate setting, finds all extreme points within expected fixed-objective time in the oracle model.
  arXiv  Detail & Related papers  (2023-06-06T05:13:29Z)
• Adaptive Federated Minimax Optimization with Lower Complexities [82.51223883622552]
  We propose an efficient adaptive minimax optimization algorithm (i.e., AdaFGDA) to solve these minimax problems. It builds our momentum-based reduced and localSGD techniques, and it flexibly incorporate various adaptive learning rates.
  arXiv  Detail & Related papers  (2022-11-14T12:32:18Z)
• Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization [108.35402316802765]
  We propose a new first-order optimization algorithm -- AcceleratedGradient-OptimisticGradient (AG-OG) Ascent. We show that AG-OG achieves the optimal convergence rate (up to a constant) for a variety of settings. We extend our algorithm to extend the setting and achieve the optimal convergence rate in both bi-SC-SC and bi-C-SC settings.
  arXiv  Detail & Related papers  (2022-10-31T17:59:29Z)
• End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location [10.130342722193204]
  Facility location problems (FLPs) are a typical class of NP-hard optimization problems, which are widely seen in the supply chain and logistics. In this paper, we consider the multi-objective facility location problem (MO-FLP) that simultaneously minimizes the overall cost and maximizes the system reliability. Two graph neural networks are constructed to learn the implicit graph representation on nodes and edges.
  arXiv  Detail & Related papers  (2022-10-27T07:15:55Z)
• A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems [9.653157073271021]
  We develop a decentralized adaptive momentum (ADAM)-type algorithm for solving min-max optimization problem. We obtain non-asymptotic rates of convergence of the proposed algorithm for finding a (stochastic) first-order Nash equilibrium point.
  arXiv  Detail & Related papers  (2021-06-10T22:32:01Z)
• Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
  We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network. We design two optimal algorithms that attain these lower bounds. We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-08T15:54:44Z)
• Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization [98.0595480384208]
  We propose a generalization extraient spaces which converges to a stationary point. The algorithm applies not only to general $p$-normed spaces, but also to general $p$-dimensional vector spaces.
  arXiv  Detail & Related papers  (2020-10-31T21:35:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.