The Big Bang-Big Crunch (BB-BC) algorithm is based on the big bang and big contraction theory of the universe. With the increase of number of iterations in optimizing of high dimensional functions

the candidates shrink slowly

worsen in quality and weaken rapidly in diversity

as well as sink into a local optimal solution. In light of these features

an improved hybrid BB-BC algorithm (HBB-BC) is proposed. This algorithm puts the center of mass into contemporary candidates computing as a singular point solution to increase the speed of contraction and improves the candidates’ quality and enhances its diversity by mean of Particle Swarm optimization (PSO). At last

Big Rip theory is introduced to increase the diversity of the big bang phase solutions and the ability to jump out of local optimal solution. The experimental results tested by 9 new benchmark test functions indicate that the improved algorithm performs better than the BB-BC and Uniform Big Bang-Chaotic Big Crunch (UBB-CBC) on optimization of high dimensional functions.