SIMULATED ANNEALING ALGORITHM FOR SELECTING SUBOPTIMAL CYCLE BASIS OF A GRAPH
Authors
Abstract:
The cycle basis of a graph arises in a wide range of engineering problems and has a variety of applications. Minimal and optimal cycle bases reduce the time and memory required for most of such applications. One of the important applications of cycle basis in civil engineering is its use in the force method to frame analysis to generate sparse flexibility matrices, which is needed for optimal analysis. In this paper, the simulated annealing algorithm has been employed to form suboptimal cycle basis. The simulated annealing algorithm works by using local search generating neighbor solution, and also escapes local optima by accepting worse solutions. The results show that this algorithm can be used to generate suboptimal and subminimal cycle bases. Compared to the existing heuristic algorithms, it provides better results. One of the advantages of this algorithm is its simplicity and its ease for implementation.
similar resources
Simulated Annealing Algorithm for Graph Coloring
The goal of this Random Walks project is to code and experiment the Markov Chain Monte Carlo (MCMC) method for the problem of graph coloring. In this report, we present the plots of cost function H by varying the parameters like q (Number of colors that can be used in coloring) and c (Average node degree). The results are obtained by using simulated annealing scheme, where the temperature (inve...
full textA new Simulated Annealing algorithm for the robust coloring problem
The Robust Coloring Problem (RCP) is a generalization of the well-known Graph Coloring Problem where we seek for a solution that remains valid when extra edges are added. The RCP is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. This problem has been proved as NP-hard and in instances larger than 30 vertices, meta-...
full textA Simulated Annealing Algorithm for Unsplittable Capacitated Network Design
The Network Design Problem (NDP) is one of the important problems in combinatorial optimization. Among the network design problems, the Multicommodity Capacitated Network Design (MCND) problem has numerous applications in transportation, logistics, telecommunication, and production systems. The MCND problems with splittable flow variables are NP-hard, which means they require exponential time t...
full textSimulated Annealing for Graph Bisection
W e resolve in the aff irmative a question of Boppana and B u i : whether simulated annealing can, w i th high probability and in polynomial t i m e , f ind the optimal bisection of a r a n d o m graph in Gnpr when p r = O(n*-’) f o r A 5 2 . ( T h e random graph model Gnpr specifies a “planted” bisection of density r , separating t w o n / 2 v e r t e x subsets of slightly higher density p . )...
full textParallel Simulated Annealing Algorithm for Graph Coloring Problem
The paper describes an application of Parallel Simulated Annealing (PSA) for solving one of the most studied NP-hard optimization problems: Graph Coloring Problem (GCP). Synchronous master-slave model with periodic solution update is being used. The paper contains description of the method, recommendations for optimal parameters settings and summary of results obtained during algorithm’s evalua...
full texta new simulated annealing algorithm for the robust coloring problem
the robust coloring problem (rcp) is a generalization of the well-known graph coloring problem where we seek for a solution that remains valid when extra edges are added. the rcp is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. this problem has been proved as np-hard and in instances larger than 30 vertices, meta-...
full textMy Resources
Journal title
volume 12 issue 2
pages 234- 243
publication date 2022-04
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023