A Dynamical Study on Probabilistic Cellular Automata Related to Whale Optimization Algorithm over Time Series Alignment Problems

Alignment of time series of different sampling length are a major topic of research. Many techniques and advance models are formed to tackle the problems. When the alignment is of the multi-objective type, it is more challenging. To answer the problem Cellular Multi-Objective Whale Optimization Algorithm has been developed. This article is based on the effect of the parameters on the performance of the model. We found, some sets of parametric values are enhancing the model, where others have negative effect, demanding more time or high computational cost. Also, we have studied the dynamics of the optimal solution in each time and discussed various cases of changes in Pareto front during the optimization process. The effect of Cellular Automata boundary conditions and neighborhoods on optimization are discussed.