A new multi-objective algorithm, pareto archived DDS

  • Authors:

  • Masoud Asadzadeh;Bryan A. Tolson

  • Affiliations:

  • University of Waterloo, Waterloo, ON, Canada;University of Waterloo, Waterloo, ON, Canada

  • Venue:
  • Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
  • Year:
  • 2009

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Abstract

The dynamically Dimensioned Search (DDS) continuous global optimization algorithm [5] is modified to solve continuous multi-objective unconstrained optimization problems. Inspired by Pareto Archived Evolution Strategy (PAES), the proposed multi-objective optimization, PA-DDS uses DDS as a search engine and archives all the non-dominated solutions during the search. In order to maintain the diversity of solutions, PA-DDS, which is single solution based, samples from less crowded parts of the external set of non-dominated solutions in each iteration. This tool inherits the parsimonious characteristic of DDS, so it has only one algorithm parameter from DDS, which does not need tuning, and one new parameter that defines the portion of computational budget for finding individual minima. PA-DDS uses crowding distance measure to sample from less populated parts of the tradeoff. The performance of the proposed tool is assessed in solving two test problems ZDT4 and ZDT6 [8] that have multiple local Pareto fronts. Results show that PA-DDS is promising relative to two high quality benchmark algorithms NSGA-II [3, 7] and AMALGAM [7].