Infeasibility Driven Evolutionary Algorithm (IDEA) for Engineering Design Optimization

  • Authors:

  • Hemant K. Singh;Amitay Isaacs;Tapabrata Ray;Warren Smith

  • Affiliations:

  • School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT,;School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT,;School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT,;School of Aerospace, Civil and Mechanical Engineering, University of New South Wales, Australian Defence Force Academy, Canberra, ACT,

  • Venue:
  • AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
  • Year:
  • 2008

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Abstract

Engineering design often requires solutions to constrained optimization problems with highly nonlinear objective and constraint functions. The optimal solutions of most design problems lie on the constraint boundary. In this paper, Infeasibility Driven Evolutionary Algorithm (IDEA) is presented that searches for optimum solutions near the constraint boundary. IDEA explicitly maintains and evolves a small proportion of infeasible solutions. This behavior is fundamentally different from the current state of the art evolutionary algorithms, which rank the feasible solutions higher than the infeasible solutions and in the process approach the constraint boundary from the feasible side of the design space. In IDEA, the original constrained minimization problem with k objectives is reformulated as an unconstrained minimization problem with k + 1 objectives, where the additional objective is calculated based on the relative amount of constraint violation among the population members. The presence of infeasible solutions in IDEA leads to an improved rate of convergence as the solutions approach the constraint boundary from both feasible and infeasible regions of the search space. As an added benefit, IDEA provides a set of marginally infeasible solutions for trade-off studies. The performance of IDEA is compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II) [1] on a set of single and multi-objective mathematical and engineering optimization problems to highlight the benefits.