CAJ | 학술논문

基于对现实中鸟群飞行方式的模拟，提出一种采用双模飞行的粒子群优化算法。该算法中的粒子在搜索过程中可使用变轨和不变轨两种飞行模式，并根据群体信息反馈和自身状态选择自己的飞行模式。文中选取典型的高维复杂优化问题作为算法优化性能测试。实验表明该算法的全局搜索能力有较大提高，能有效避免早熟收敛问题，可用于求解高维的复杂优化问题。
기우대현실중조군비행방식적모의，제출일충채용쌍모비행적입자군우화산법。해산법중적입자재수색과정중가사용변궤화불변궤량충비행모식，병근거군체신식반궤화자신상태선택자기적비행모식。문중선취전형적고유복잡우화문제작위산법우화성능측시。실험표명해산법적전국수색능력유교대제고，능유효피면조숙수렴문제，가용우구해고유적복잡우화문제。
An optimization algorithm is proposed based on the simulation of flight modes of the real birds, namely particle swarm optimization algorithm with double-flight modes ( DMPSO). Particles can use maneuver flight-mode or non-maneuver flight-mode to fly during searching. Each particle chooses its flight-mode according to the feedback of the swarm information and its own state in the search. To test the performance of DMPSO, experiments are carried out on some typical complex high-dimensional optimization problems. The experimental results show that the DMPSO avoids the premature convergence problems and it is effective when solving complex high dimensional optimization problems.