Lifecycle-based Swarm Optimization Method for Constrained Optimization

Each  biologic  must  go  through  a  process  from birth, growth, reproduction until death, this process known as  life  cycle.  This  paper  borrows  the  biologic  life  cycle theory  to  propose  a  Lifecycle-based  Swarm  Optimization (LSO) algorithm. Based on some features of  life cycle, LSO designs six optimization operators: chemotactic, assimilation, transposition,  crossover,  selection  and  mutation.  In  this paper,  the  capability  of the  LSO  to  address  constrained optimization problem was investigated. Firstly, the proposed method  was  test  on  some  well-known  and  widely  used benchmark  problems. When compared  with  PSO,  we  can see  that  LSO  can  obtain  the  better  solution  and  lower standard  deviation  than  PSO  on  many  different  types  of constrained  optimization problems.  Finally,  LSO  was  also used for seeking the optimal route for vehicle route problem in  logistics  system.  The  result  of  LSO  is  the  best  when comparing  with PSO  and  GA.  The  results  of  above  two types  of  experiments, which  include  not  only  the  ordinary benchmark  problem  but  also  the  practical  problems  in engineering, demonstrate  that  LSO  is  a  competitive  and effective approach for solving constrained problems.