Dominance-Based Multiobjective
Kevin Smith, Richard Everson, Jonathan Edward Fieldsend, Chris Murphy, Rashmi Misra · 2008
Simulated annealing is a provably convergent opti- mizer for single-objective problems. Previously proposed multiob- jective extensions have mostly taken the form of a single-objective simulated annealer optimizing a composite function of the objec- tives. We propose a multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for opti- mization,eliminatingproblemsassociatedwithcompositeobjective functions. We also propose a method for choosing perturbation scalingspromotingsearchbothtowardsandacrosstheParetofront. We illustrate the simulated annealer's performance on a suite of standard test problems and provide comparisons with another multiobjective simulated annealer and the NSGA-II genetic algo- rithm. The new simulated annealer is shown to promote rapid con- vergence to the true Pareto front with a good coverage of solutions across it comparing favorably with the other algorithms. An application of the simulated annealer to an industrial problem, the optimization of a code-division-multiple access (CDMA) mobile telecommunications network's air interface, is presented and the simulated annealer is shown to generate nondominated solutions with an even and dense coverage that outperforms single objective genetic algorithm optimizers.