A novel chemo-inspired GA for solving constrained optimization problem

In this paper, a novel hybridized algorithm is developed to solve constrained optimization real life problem. The newly developed algorithm is introduced in the name of Chemo-inspired Genetic Algorithm for constrained optimization (CGAC). Here, one typical engineering problem is solved by CGAC and the numerical results are compared with Differential Evolution with Level Comparison (DELC), Differential Evolution with Dynamic Stochastic Selection (DEDS), Hybrid Evolutionary Algorithm and Adaptive constraint-handling technique (HEAA) and many other evolutionary algorithms. The computational result confirms the out per performance of CGAC over others.