Pruned Search: A Machine Learning Based Meta-Heuristic Approach for Constrained Continuous Optimization

Searching for solutions that optimize a continuous function can be difficult due to the infinite search space, and can be further complicated by the high dimensionality in the number of variables and complexity in the structure of constraints. Both deterministic and stochastic methods have been presented in the literature with a purpose of exploiting the search space and avoiding local optima as much as possible. In this research, we develop a machine learning framework aiming to 'prune' the search effort of both types of optimization techniques by developing meta-heuristics, attempting to knowledgeably reordering the search space and reducing the search region. Numerical examples demonstrate that this approach can effectively find the global optimal solutions and significantly reduce the computational time for seven benchmark problems with variable dimensions of 100, 500 and 1000, compared to Genetic Algorithms.