A Novel Surrogate-assisted Differential Evolution for Expensive Optimization Problems with both Equality and Inequality Constraints

摘要

Surrogates have recently shown excellent abilities in assisting evolutionary algorithms for solving computationally expensive constrained optimization problems (ECOPs). However, the effectiveness of such surrogate-assisted evolutionary algorithms has only been verified on ECOPs with inequality constraints. In this paper, a Novel Surrogate-Assisted Differential Evolution (NSADE) algorithm is proposed for solving ECOPs with equality and inequality constraints, in which a trial vector generation mechanism and two surrogate-assisted local search phases are carried out iteratively. The trial vector generation mechanism based on information exchange between the historical elite solution set and current population is utilized to balance exploiting potential areas and exploring unknown areas. Then the expectation improvement-based local search is used to not only guide the current population to move towards feasible region but also alleviate the inaccuracy of the surrogate on the constraint boundary. Finally, a solution identification-based local search is utilized to further optimize two different types of historical elite solutions. Empirical studies on fifteen widely used benchmark problems demonstrate that the proposed NSADE can effectively obtain high-quality feasible solutions on ECOPs with equality constraints under a limited computational budget.