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Adra, S.F.; Griffin, I.A.; Fleming, P.J. (2007)
Publisher: Automatic Control and Systems Engineering, University of Sheffield
Languages: English
Types: Book
Subjects:

Classified by OpenAIRE into

arxiv: Computer Science::Neural and Evolutionary Computation
A novel multiobjective optimisation accelerator is\ud introduced that uses direct manipulation in objective space\ud together with neural network mappings from objective space to decision space. This operator is a portable component that can be hybridized with any multiobjective optimisation algorithm. The purpose of this Convergence Acceleration Operator (CAO) is to enhance the search capability and the speed of convergence of the host algorithm. The operator acts directly in objective space to suggest improvements to solutions obtained by a multiobjective evolutionary algorithm (MOEA). These suggested improved objective vectors are then mapped into decision variable space and tested. The CAO is incorporated with two leading MOEAs, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm (SPEA2) and tested. Results show that the hybridized algorithms consistently improve the speed of convergence of the original algorithm whilst maintaining the desired distribution of solutions.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • [2] Fleming, P., R.C. Purshouse, and R.J. Lygoe. Many-Objective Optimization: An Engineering Design Perspective. in Evolutionary MultiCriterion Optimization. Third International Conference, EMO 2005. 2005. Guanajuato, Mexico: Springer. Lecture Notes in Computer Science Vol. 3410.
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