A Novel Improved Bird Swarm Algorithm for Solving Bound Constrained Optimization Problems

2019 ◽  
Vol 24 (4) ◽  
pp. 349-359 ◽  
Author(s):  
Yuhe Wang ◽  
Zhongping Wan ◽  
Zhenhua Peng



2014 ◽  
Vol 670-671 ◽  
pp. 1517-1521
Author(s):  
Tie Bin Wu ◽  
Tao Yun Zhou ◽  
Wen Li ◽  
Gao Feng Zhu ◽  
Yun Lian Liu

A particle swarm algorithm (PSO) based on boundary buffering-natural evolution was proposed for solving constrained optimization problems. By buffering the particles that cross boundaries, the diversity of populations was intensified; to accelerate the convergence speed and avoid local optimum of PSO, natural evolution was introduced. In other words, particle hybridization and mutation strategies were applied; and by combining the modified feasible rules, the constrained optimization problems were solved. The simulation results proved that the method was effective in solving this kind of problems.





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