Software testing has faced many intractable problems: for real world programs, the number of possible input combinations can exceed the number of atoms in the ocean, so as a practical matter it is impossible to show through that the program works correctly for all inputs. Combinatorial testing offers a solution. Combinatorial testing of software analyzes interactions among variables using a very small number of tests. It can help to detect the problem early in the testing life cycle. Artificial Life techniques can dramatically change our ability to solve a host of problems in applied science and engineering; many search techniques have been developed and applied successfully in many fields. In this paper we had shown different variants from existing search algorithms: Genetic Algorithm, Particle Swarm Optimization and Ant Colony Algorithm, Bee colony Optimization, Simulate Annealing. Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. We believe that these search techniques can be further improved by fine-tuning their configuration and used in broad ranges of area.