Solving combinatorial optimization problems using augmented lagrange chaotic simulated annealing
Soong, Boon Hee
Date of Issue2011
School of Electrical and Electronic Engineering
Chaotic simulated annealing (CSA) proposed by Chen and Aihara has been successfully used to solve a variety of combinatorial optimization problems. CSA uses a penalty term to enforce solution validity as in the original Hopfield–Tank approach. There exists a conflict between solution quality and solution validity in the penalty approach. It is often difficult to adjust the relative magnitude of the penalty term, so as to achieve a high quality solution which is at the same time valid. To overcome this, we incorporate augmented Lagrange multipliers into CSA, obtaining a method that we call augmented Lagrange chaotic simulated annealing (AL-CSA). Simulation results on two constrained optimization benchmarks derived from the Hopfield–Tank formulation of the traveling salesman problem show that AL-CSA can maintain CSA’s good solution quality while avoiding the potential difficulties associated with penalty terms. Furthermore, AL-CSA’s convergence time is shorter and choice of system parameters is easier compared to CSA.
DRNTU::Engineering::Electrical and electronic engineering
Differential equations and dynamical systems
© 2011 Foundation for Scientific Research and Technological Innovation. This is the author created version of a work that has been peer reviewed and accepted for publication by Differential Equations and Dynamical Systems, FSRTI. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/s12591-011-0084-4].