Beluga Whale Optimization (BWO) is a recently developed meta-heuristics search algorithm to provide good balance between the exploration phase and the exploitation phase in solving benchmark optimization problems. However, the local search of the basic BWO algorithm has slow convergence rate due to its poor exploitation capability. We proposed a hybrid algorithm using a chaotic variant of the present optimization algorithm in order to enhance its exploitation ability and abbreviated as CBWO. To appraise the performance of CBWO, it is first verified on 23 standard benchmark functions. A comparative study has been done that shows the advantage of the proposed algorithm and associated with a number of existing algorithms. Simulation results were carried out on eleven classical engineering problems. Pseudo code of CBWO algorithm is presented in paper. Results come to know that CBWO could be more effective in optimization with quicker and advanced convergence rate and accuracy.