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

作者：Georgopoulou Chariklia A; Giannakoglou Kyriakos C^*

来源：Engineering Optimization, 2009, 41(10): 909-923.

DOI：10.1080/03052150902866577

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

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.

• 出版日期2009