Improvement And Application Of Wolf Pack Algorithm

Noticing the shortcomings of Wolf Pack Algorithm(WPA),such as too many parameters,predetermined step length and fixed scouting direction,a wolf pack algorithm based on adaptive step length and regulable scouting direction,named Modified Adaptive and Changed Scouting Direction Wolf Pack Algorithm(MACWPA)is proposed.It makes modifications on the step length of the three major processes,namely scouting behavior,summoning behavior and beleaguering behavior,and adopts tentative direction of scouting behavior,providing wolf pack more artificial intelligence.Each wolf is able to adjust its step length as well as scouting direction according to the leader wolf's position,which simplifies parameter set up,accelerates the convergence speed and improves the optimization precision.Simulation results show that the optimization precision for low dimensional unimodal function is greatly improved by MACWPA compared with WPA.It also improves the optimization precision of high dimensional Multimodal function.Binary wolf pack algorithm(BWPA)is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA,an improved binary wolf pack algorithm(AIBWPA)can be proposed by adopting adaptive step length and improved update strategy of wolf pack.AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA,DPSO,which proves that AIBWPA has higher optimization accuracy and better computational robustness.AIBWPA makes the parameters simple,protects the population diversity,enhances the global convergence.