CAJ | 학술논문

自然界中生命体都存在着有限的生命周期，随着时间的推移生命体会出现老化并死亡的现象，这种老化机制对于生命群体进化并保持多样性有重要影响。针对量子行为粒子群（QPSO）算法中粒子存在老化并使得算法存在早熟收敛的现象，将生命体的自我更新机制引入了QPSO算法，在粒子群体进化中提出领导者粒子和挑战者粒子，随着群体粒子的老化，当领导者粒子领导力耗尽不能引导群体进化时，挑战者粒子通过竞争更新机制成为新的领导者粒子引导群体进化并保持群体多样性，并证明了算法的全局收敛性。将提出的算法与多种典型改进QPSO算法通过12个CEC2005 benchmark测试函数进行比较，对结果进行了分析。仿真结果显示，该算法具有较强的全局搜索能力，尤其在7个多峰测试函数中，综合性能最优。
자연계중생명체도존재착유한적생명주기，수착시간적추이생명체회출현노화병사망적현상，저충노화궤제대우생명군체진화병보지다양성유중요영향。침대양자행위입자군（QPSO）산법중입자존재노화병사득산법존재조숙수렴적현상，장생명체적자아경신궤제인입료QPSO산법，재입자군체진화중제출령도자입자화도전자입자，수착군체입자적노화，당령도자입자령도력모진불능인도군체진화시，도전자입자통과경쟁경신궤제성위신적령도자입자인도군체진화병보지군체다양성，병증명료산법적전국수렴성。장제출적산법여다충전형개진QPSO산법통과12개CEC2005 benchmark측시함수진행비교，대결과진행료분석。방진결과현시，해산법구유교강적전국수색능력，우기재7개다봉측시함수중，종합성능최우。
Life body has limited life in nature;it will be aging and die with time. The aging mechanism is very important to keep swarm diversity during evolutionary process. For the phenomenon that Quantum-behaved Particle Swarm Optimi-zation(QPSO)is often premature convergence, self-renewal mechanism is proposed into QPSO, and a leading particle and challengers are introduced. When the leading power of leading particle is exhausted, one challenger will select to be the new leading particle and continues keeping the diversity of swarm with a certain renewal mechanism. Furthermore, global convergence of the proposed algorithm is proved. Finally, the comparison and analysis of results with the proposed method and classical improved QPSO algorithm based on twelve CEC2005 benchmark function is given, the simulation results show stronger global searching ability of the modified algorithm. Especially in the seven multi-model test func-tions, the comprehensive performance is optimal.