计算机应用与软件
計算機應用與軟件
계산궤응용여연건
Computer Applications and Software
2015年
8期
248-251,299
,共5页
群体智能%烟花爆炸搜索%动态随机搜索%佳点集
群體智能%煙花爆炸搜索%動態隨機搜索%佳點集
군체지능%연화폭작수색%동태수궤수색%가점집
Swarm intelligence%Fireworks explosion search%Dynamic random search%Good-point set
为了克服烟花爆炸搜索算法容易早熟的弱点，提高其求解性能，提出一种融合佳点集变异机制的动态搜索烟花爆炸算法。首先为了提高算法的求解精度，每一次迭代过程均针对当前最佳个体执行动态随机搜索，加强对当前最佳的局部搜索。另一方面，当种群的拥挤程度超越设定的阈值λ时，除保留10％的优秀个体外，其余个体基于佳点集机制进行重新初始化，帮助种群摆脱局部最优的约束。最后，在6个Benchmark函数上的实验表明，该算法能快速收敛、克服早熟，并且具有较佳的鲁棒性。
為瞭剋服煙花爆炸搜索算法容易早熟的弱點，提高其求解性能，提齣一種融閤佳點集變異機製的動態搜索煙花爆炸算法。首先為瞭提高算法的求解精度，每一次迭代過程均針對噹前最佳箇體執行動態隨機搜索，加彊對噹前最佳的跼部搜索。另一方麵，噹種群的擁擠程度超越設定的閾值λ時，除保留10％的優秀箇體外，其餘箇體基于佳點集機製進行重新初始化，幫助種群襬脫跼部最優的約束。最後，在6箇Benchmark函數上的實驗錶明，該算法能快速收斂、剋服早熟，併且具有較佳的魯棒性。
위료극복연화폭작수색산법용역조숙적약점，제고기구해성능，제출일충융합가점집변이궤제적동태수색연화폭작산법。수선위료제고산법적구해정도，매일차질대과정균침대당전최가개체집행동태수궤수색，가강대당전최가적국부수색。령일방면，당충군적옹제정도초월설정적역치λ시，제보류10％적우수개체외，기여개체기우가점집궤제진행중신초시화，방조충군파탈국부최우적약속。최후，재6개Benchmark함수상적실험표명，해산법능쾌속수렴、극복조숙，병차구유교가적로봉성。
For overcoming the weakness of firework explosion search ( FES) algorithm in being prone to prematurity and improving its solu-tion performance, we proposed in this paper a dynamic firework explosion search algorithm which integrates the mutation mechanism of good-point set.First, in order to improve the precision of solution, every iteration process was targeted at current best individual to execute dynam-ic random search and this enhanced the local search for the current best.On the other hand, when the overcrowding degree of population ex-ceeded the preset thresholdλ, all the individuals, except 10%excellent ones remained, were to reinitialise based on good-point set mecha-nism to help the population get rid of the constraint from local optimum.In the end, the experiments on six classical Benchmark functions demonstrated that the improved FES algorithm could fast converge, prevented the prematurity and had better robustness.
