Journal ArticleDOI
Application of chaos in simulated annealing
Ji Mingjun,Tang Huan-wen +1 more
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TLDR
Simulation results of typical complex function optimization show that CSA improves the convergence and is efficient, applicable and easy to implement.Abstract:
Simulated annealing (SA) has been applied with success to many numerical and combinatorial optimization problems in recent years. SA has a rather slow convergence rate, however, on some function optimization problems. In this paper, by introducing chaotic systems to simulated annealing, we propose a optimization algorithm named chaos simulated annealing (CSA). The distinctions between CSA and SA are chaotic initialization and chaotic sequences replacing the Gaussian distribution. Simulation results of typical complex function optimization show that CSA improves the convergence and is efficient, applicable and easy to implement. In addition, we discuss the advantages of CSA, and show the reasons why CSA performs better than SA.read more
Citations
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Improved particle swarm optimization combined with chaos
TL;DR: Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality.
Journal ArticleDOI
Firefly algorithm with chaos
TL;DR: This study introduces chaos into FA so as to increase its global search mobility for robust global optimization and shows that some chaotic FAs can clearly outperform the standard FA.
Journal ArticleDOI
A survey of simulated annealing as a tool for single and multiobjective optimization
Balram Suman,Prabhat Kumar +1 more
TL;DR: A comprehensive review of simulated annealing (SA)-based optimization algorithms, which solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima.
Journal ArticleDOI
Chaotic bat algorithm
Amir H. Gandomi,Xin-She Yang +1 more
TL;DR: Chaos is introduced into Bat algorithm so as to increase its global search mobility for robust global optimization and results show that some variants of chaotic BAs can clearly outperform the standard BA for these benchmarks.
Global optimization and simulated annealing
TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Journal ArticleDOI
Deterministic nonperiodic flow
TL;DR: In this paper, it was shown that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states, and systems with bounded solutions are shown to possess bounded numerical solutions.
Book
From Chaos To Order Methodologies, Perspectives and Applications
Guanrong Chen,Xiaoning Dong +1 more
TL;DR: This work focuses on chaos and order ordering chaos organization of the monograph and some applications of controlling chaos, and some approaches to controlling chaos.
Book
Facility Layout and Location: An Analytical Approach
TL;DR: In this article, the authors propose a planar multifacility location model to solve the problem of plant layout in a single facility and a cyclic network in a multi-modal network.