Research Article
A Novel Particle Swarm Optimization Algorithm for Global Optimization
Algorithm 1
Framework of NPSO. Remark: in our algorithm, for simplicity, in (
8), we can set
, which can meet our needs.
(1) Initialize a population of particles with random positions in a given search space, and random | velocities ; the maximum iteration ; ; ; the length of chaotic sequence . | (2) Set and find . | (3) while do | (4) | (5) for to do | (6) for to do | (7) By (3) and (4), update the velocity of each particle. | (8) By (5), update the position of each particle. | (9) end for | (10) if | (11) , set ; | (12) else | (13) set . | (14) end if | (15) if | (16) set , | (17) end if | (18) end for | (19) for to do | (20) if | (21) By (8), to generate a new position, and replace . | (22) end if | (23) end for | (24) By (9)–(12), to chaotic search in , and update (if necessary). | (25) | (26) end while |
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