CAJ | 학술논문

针对野草算法存在求解精度不高、收敛速度慢的问题，提出一种基于A lo pex的野草算法。在原有野草算法框架的空间扩散阶段引入Alopex算法，通过从父代和子代个体自变量和目标函数值的变化情况获得启发信息，指导种群向最优方向进化。结合后的算法能够充分发挥两者的优点，改善野草算法收敛速度以及寻优精度。对典型基准函数的测试结果表明，该算法要优于基本野草算法，表现更为稳定，体现出较好的全局搜索能力，具有更快的收敛速度和更高的寻优精度，更适合于解决其它算法难以解决的高维多峰值函数的优化问题；通过与其它相关智能算法的比较，进一步验证了该算法的有效性。
침대야초산법존재구해정도불고、수렴속도만적문제，제출일충기우A lo pex적야초산법。재원유야초산법광가적공간확산계단인입Alopex산법，통과종부대화자대개체자변량화목표함수치적변화정황획득계발신식，지도충군향최우방향진화。결합후적산법능구충분발휘량자적우점，개선야초산법수렴속도이급심우정도。대전형기준함수적측시결과표명，해산법요우우기본야초산법，표현경위은정，체현출교호적전국수색능력，구유경쾌적수렴속도화경고적심우정도，경괄합우해결기타산법난이해결적고유다봉치함수적우화문제；통과여기타상관지능산법적비교，진일보험증료해산법적유효성。
Concerning the low accuracy in application and the slow rate of convergence of invasive weed optimization ,a new inva‐sive weed optimization (IWO) based on the algorithm of pattern extraction (Alopex) was proposed .The Alopex was imported in the spatial dispersal of IWO’s base framework .The inspired information was obtained from changes of individuals making the population evolve in the direction of the global optimal .Based on the novel and distinct qualifications of IWO and Alopex ,the AI‐WO algorithm was introduced and their excellent features in this extended algorithm were combined .The efficiency of this algo‐rithm in the speed of convergence and the optimality of results were compared with IWO through a number of common bench‐mark functions .The results of experiment reveal that the proposed algorithm is more stable than IWO and shows better capabili‐ty of global search ,which makes it suitable for the optimization of multi‐dimensional functions of which some other algorithms are incapable .Besides the experiment results ,comparing with some other related intelligent algorithms also give further confir‐mation of the effectiveness of the proposed algorithm .