A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement

Abstract

Metamodel-assisted evolutionary algorithms are low-cost optimization methods for CPU-demanding problems. Memetic algorithms combine global and local search methods, aiming at improving the quality of promising solutions. This article proposes a metamodel-assisted memetic algorithm which combines and extends the capabilities of the aforementioned techniques. Herein, metamodels undertake a dual role: they perform a low-cost pre-evaluation of population members during the global search and the gradient-based refinement of promising solutions. This reduces significantly the number of calls to the evaluation tool and overcomes the need for computing the objective function gradients. In multi-objective problems, the selection of individuals for refinement is based on domination and distance criteria. During refinement, a scalar strength function is maximized and this proves to be beneficial in constrained optimization. The proposed metamodel-assisted memetic algorithm employs principles of Lamarckian learning and is demonstrated on mathematical and engineering applications.

Keywords:

• metamodel-assisted memetic algorithms
• fitness function
• strength
• multi-objective optimization

Acknowledgements

This work was co–funded by the PENED03 program (Measure 8.3 of the Operational Program Competitiveness, of which 80% is European Commission and 20% national funding), under project number 03ED111.