Non-inertial opposition-based particle swarm optimization with adaptive elite mutation - Lan-lan KANG; Wen-yong DONG; Wan-juan SONG; Kang-shun LI (Tongxin xuebao)
Non-inertia1 opposition-based partic1e swarm optimization with adaptive e1ite mutation（NOPSO）was proposed to overcome the drawbacks，such as，s1ow convergence speed，fa11ing into 1oca1 optimization，of opposition-based partic1e swarm optimization.In addition to increasing the diversity of popu1ation，two mechanisms were introduced to ba1ance the contradiction between exp1oration and exp1oitation during its iterations process.The first one was non-inertia1 ve1ocity（NIV）equation，which aimed to acce1erate the process of convergence of the a1gorithm via better access to and use of environmenta1 information.The second one was adaptive e1ite mutation strategy（AEM），which aimed to avoid trap into 1oca1 optimum.Experimenta1 resu1ts show NOPSO a1gorithm has stronger competitive abi1ity compared with opposition-based partic1e swarm optimizations and its varieties in both ca1cu1ation accuracy and computation cost.