CAJ | 학술논문

生物地理学优化算法是一种新的全局优化算法,但该算法存在搜索能力不强的缺点.针对此不足,提出一种基于进化规划算法的实数编码混合生物地理学优化算法,新算法将进化规划的搜索性与生物地理学优化算法的利用性进行有机结合,从而达到搜索性与利用性的平衡.通过13个高维标准测试函数对算法进行测试,验证了新算法的有效性.与基本生物地理学优化算法和两种经典的进化规划算法进行比较,结果表明新算法优于所比较的三种算法.此外,新算法在收敛速度上优于基本生物地理学优化算法.
생물지이학우화산법시일충신적전국우화산법,단해산법존재수색능력불강적결점.침대차불족,제출일충기우진화규화산법적실수편마혼합생물지이학우화산법,신산법장진화규화적수색성여생물지이학우화산법적이용성진행유궤결합,종이체도수색성여이용성적평형.통과13개고유표준측시함수대산법진행측시,험증료신산법적유효성.여기본생물지이학우화산법화량충경전적진화규화산법진행비교,결과표명신산법우우소비교적삼충산법.차외,신산법재수렴속도상우우기본생물지이학우화산법.
Biogeography-based optimization（BBO） algorithm is a new global optimization algorithm. However,BBO lacks the explorative ability.In this paper,we proposed a novel hybrid BBO approach, called BBO-EP,which is characterized by 1） representing the individual as a real-coded parameter vector, and 2） combining Evolutionary Programming（EP） and BBO to enhance the explorative ability of BBO. Experiments have been conducted on 13 high-dimensional benchmark functions.And the results indicate the good performance of BBO-EP.Compared with the original BBO and the two EP approaches（FEP and CEP）,experimental results show that our approach is better than the other approaches（BBO,FEP, and CEP） in terms of the quality of the final solutions.Moreover,the proposed BBO-EP is faster than the original BBO with respect to the convergence speed.