*2.3. Hybrid Algorithm*

The hybridization of the metaheuristic algorithm plays a vital role in improving their performance. The fundamental principle of hybridization is to blend the best features of two or more metaheuristic algorithms to improve search capability, accuracy, and convergence speed of an individual algorithm. A hybrid algorithm is also known as a memetic algorithm. In the last few years, researchers have proposed different strategies for hybridizing metaheuristic algorithms. The three most explored methodologies of hybridization are multi-stage, sequential and parallel.

In the multi-stage methodology, one of the algorithms globally explores the search space and the second algorithm locally discovers the optimal solution. In sequential search, both the algorithms run sequentially and find the optimal solution in the search space. In the parallel mode, both the algorithms run parallel on the same population of the defined problem.
