CAJ | 학술논문

为了解决带有约束的桁架结构的多目标优化问题，本文采用了一种基于微分演化的多目标优化（DEMO）方法。DEMO 方法采用多目标优化进化算法中 Pareto 和拥挤距离排序机制，并保留了 DE 算法的优点。为了验证 DEMO 算法的可行性和有效性，对经典桁架进行尺寸优化，并与其他优化方法进行了比较，数值结果表明DEMO 算法性能比其他算法要好，其所得的解具有更好的多样性、均匀性和收敛性。
위료해결대유약속적항가결구적다목표우화문제，본문채용료일충기우미분연화적다목표우화（DEMO）방법。DEMO 방법채용다목표우화진화산법중 Pareto 화옹제거리배서궤제，병보류료 DE 산법적우점。위료험증 DEMO 산법적가행성화유효성，대경전항가진행척촌우화，병여기타우화방법진행료비교，수치결과표명DEMO 산법성능비기타산법요호，기소득적해구유경호적다양성、균균성화수렴성。
In order to solve the multi-objective optimization of truss structures with constrains, a new approach to multi-objective optimization based on differential evolution (DEMO) was adopted in this paper. DEMO adopted the mechanisms of Pareto based ranking and crowding distance sorting which used by evolutionary algorithms for multi-objective optimization, and preserved the advantages of differential evolution (DE). Classical truss sizing optimization problems are solved to demonstrate the feasibility and effectiveness of the DEMO algorithm, and the results are compared with other optimization methods. The results indicate that the DEMO provides better performance in the diversity, the uniformity and the convergence of the obtained solution than other methods.