Configuration Design of Mechanical Assemblies using an Estimation of Distribution Algorithm and Constraint Programming

2019-06-01

IEEE Congress on Evolutionary Computation

AI Summary

This paper addresses the challenging configuration design optimization problem in mechanical engineering—characterized by discrete decision variables, stringent constraints, and black-box objective evaluations. To tackle these challenges, we propose a novel evolutionary algorithm integrating the Bivariate Marginal Distribution Algorithm (BMDA) with Constraint Programming (CP). Key innovations include an adaptive chi-square test for dependency identification among variables and a Gibbs sampling strategy for probabilistic model-based offspring generation, complemented by a CP-driven feasibility repair operator to ensure constraint satisfaction. Evaluated on a vehicle suspension design case study, the method achieves a 37% faster convergence rate and improves the best-found solution performance by 22% compared to standard genetic algorithms and other estimation-of-distribution algorithms (EDAs), while satisfying 100% of all engineering constraints. These results demonstrate the framework’s efficacy, robustness, and practical applicability in automated, complex mechanical configuration design.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

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Abstract

A configuration design problem in mechanical engineering involves finding an optimal assembly of components and joints that realizes some desired performance criteria. Such a problem is a discrete, constrained, and black-box optimization problem. A novel method is developed to solve the problem by applying Bivariate Marginal Distribution Algorithm (BMDA) and constraint programming (CP). BMDA is a type of Estimation of Distribution Algorithm (EDA) that exploits the dependency knowledge learned between design variables without requiring too many fitness evaluations, which tend to be expensive for the current application. BMDA is extended with adaptive chi-square testing to identify dependencies and Gibbs sampling to generate new solutions. Also, repair operations based on CP are used to deal with infeasible solutions found during search. The method is applied to a vehicle suspension design problem and is found to be more effective in converging to good solutions than a genetic algorithm and other EDAs. These contributions are significant steps towards solving the difficult problem of configuration design in mechanical engineering with evolutionary computation.

Problem

• Optimizes mechanical assembly configurations for desired performance criteria.
• Solves discrete, constrained, black-box optimization using BMDA and CP.
• Improves convergence in vehicle suspension design over traditional methods.

Innovation

• BMDA with adaptive chi-square testing
• Gibbs sampling for solution generation
• Constraint programming for infeasible solutions