A Multi-Objective Metamodel-Assisted Memetic Algorithm with Strength-based Local Refinement

Kyriakos C. Giannakoglou (1) , Chariklia Georgopoulou (1)

1 NTUA - National Technical University of Athens [Athens]

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 paper 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.

Mots clés: Physical Sciences

Kyriakos C. Giannakoglou, Chariklia Georgopoulou. A Multi-Objective Metamodel-Assisted Memetic Algorithm with Strength-based Local Refinement. Engineering Optimization, 2009, 41 (10), pp.909-923. ⟨10.1080/03052150902866577⟩. ⟨hal-00545364⟩