Cellular Processing Algorithms.

In this chapter we propose a new class of cellular algorithms. There exists a variety of cellular algorithm approaches but most of them do not structure the search process. In this work we propose a cellular processing approach to solve optimization problems. The main components of these algorithms are: the processing cells (PCells), the communication between PCells, and the global and local stagnation detection. The great flexibility and simplicity of this approach permits pseudo-parallelization of one or several different metaheuristics. To validate our approach, the linear ordering problem with cumulative costs (LOPCC) was used to describe two cellular processing algorithms, whose performance was tested with standard instances. The experimental results show that the cellular processing algorithms increase solution quality up to 3.6% and reduce time consumption up to 20% versus the monolithic approach. Also the performance of these algorithms is statistically similar to those of the state-of-the-art solutions, and they were able to find 38 new best-known solutions (i.e., not previously found by other algorithms) for the instances used. Finally, it is important to point out that these encouraging results indicate that the field of cellular processing algorithms is a new and rich research area.