The paper propose a widely used nature inspired meta-heuristic algorithm named as chemical reaction optimization (CRO_SCS) algorithm to solve a well-known NP-hard combinatorial problem shortest common supersequence (SCS).

Four basic reaction operators are used for exploring solution space and Reform function is used for checking the constraints of newly formed supersequence and repairing them if any violation occurs.

Average length of returned supersequence, average execution time and average standard deviation of proposed CRO_SCS algorithm are compared with enhanced beam search (IBS_SCS), ant colony optimization (ACO), deposition and reduction (DR) and artificial bee colony (ABC) algorithms.

Out of 26 instances of random and real datasets, in 24 cases CRO_SCS algorithm shows better result in average length of returned supersequence than all other algorithms.

In case of average execution time, CRO_SCS has better result in 9 cases and ABC algorithm shows better result in 17 cases although ABC algorithm cannot ensure supersequence properties.

Besides, in case of average standard deviation CRO_SCS shows better results than all other nondeterministic algorithms.