CAJ | 학술논문

人工蜂群算法中的侦察蜂阶段的搜索操作在一定程度上可以解决算法陷入局部最优的问题，但也和其他启发式优化算法一样，存在着局部搜索能力差，在接近最优解时搜索效率下降，以及求解复杂问题时可能陷入局部最优而使算法停滞等缺陷。为了改善此缺陷，采用 NM算法来取代人工蜂群算法侦察蜂阶段的随机产生个体机制，提出了一种基于 NM算法的改进人工蜂群算法(NMABC)。希望基于 NM算法优异的局部搜寻能力，改善人工蜂群算法局部搜索能力较差之缺陷并提高搜索效率。
인공봉군산법중적정찰봉계단적수색조작재일정정도상가이해결산법함입국부최우적문제，단야화기타계발식우화산법일양，존재착국부수색능력차，재접근최우해시수색효솔하강，이급구해복잡문제시가능함입국부최우이사산법정체등결함。위료개선차결함，채용 NM산법래취대인공봉군산법정찰봉계단적수궤산생개체궤제，제출료일충기우 NM산법적개진인공봉군산법(NMABC)。희망기우 NM산법우이적국부수심능력，개선인공봉군산법국부수색능력교차지결함병제고수색효솔。
Artificial bee colony algorithm in the scout bee stage search operation can solve the al-gorithm into local optimal solution to a certain extent,but like other heuristic optimization algorithm, there exists a poor local search ability,when near optimal solution search efficiency,it is likely to fall into local optimum with algorithm stagnation problems in solving complex problems.In order to rem-edy this defect,this paper uses NM algorithm to replace the artificial bee colony algorithm scout bee stage of randomly generated individual mechanism,proposes an improved artificial bee colony algo-rithm based on NM (NMABC)expecting NM algorithm based on excellent local search capability and artificial bee colony algorithm to improve the poor local search ability with defect,thus to improve search efficiency.