ADVANCED SOLUTION METHODS IN STRUCTURAL OPTIMIZATION-BASED ON EVOLUTION STRATEGIES

The objective of this paper is to investigate the efficiency of hybrid solution methods when incorporated into large-scale optimization prob lems solved by evolution strategies (ESs) and to demonstrate their inf luence on the overall performance of these optimization algorithms. ES s imitate biological evolution and combine the concept of artificial s urvival of the fittest with evolutionary operators to form a robust se arch mechanism. In this paper modified multi-membered evolution strate gies with discrete variables are adopted. Two solution methods are imp lemented based on the preconditioned conjugate gradient (PCG) algorith m. The first method is a PCG algorithm with a preconditioner resulted from a complete Cholesky factorization, and the second is a PCG algori thm in which a truncated Neumann series expansion is used as a precond itioner. The numerical tests presented demonstrate the computational a dvantages of the proposed methods, which become more pronounced in lar ge-scale optimization problems and in a parallel computing environment .