Improved evolutionary optimization from genetically adaptive multimethod search

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Fig. 1.
Fig. 1.

Generated Pareto-optimal fronts after 25, 50, and 75 generations with the NSGA-II (squares), PSO (circles), AMS (+), DE (diamonds), and AMALGAM (x) optimization algorithms for test problem ZDT4. This benchmark problem has 219 different local Pareto-optimal fronts in the search space, of which only one corresponds to the global Pareto-optimal front. Combining the individual algorithms into a simultaneous multimethod search algorithm ensures a faster and more reliable solution to multiobjective optimization problems.


This Article

  1. Published online before print January 10, 2007, doi: 10.1073/pnas.0610471104 PNAS January 16, 2007 vol. 104 no. 3 708-711
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