A Subset Simulation algorithm for structural optimization

A Subset Simulation algorithm is proposed for deterministic structural design optimization under general multiple constraints. The proposed algorithm is based on the idea that an extreme event (optimization problem) can be considered as a rare event (reliability problem). It is population-based realized with Markov Chain Monte Carlo and a simple evolutionary strategy. Problem-specific constraints are handled by a feasibility-based fitness function that reflects their degree of violation. Based on the constraint fitness function, a double-criterion sorting algorithm is used to guarantee that any feasible solutions are given a higher priority over the infeasible ones before their objective function values are ranked. The efficiency and robustness of the proposed algorithm are illustrated using a benchmark optimization design problems. Comparison is made with other well-known stochastic optimization algorithms, such as Genetic Algorithm, Particle Swarm Optimization and Harmony Search.