Changes in environment are common in daily activities and can introduce new problems. To be adaptive to these changes, new solutions are to be found every time change occur. This two-part paper employs a technique called Centroid-Based Adaptation (CBA) which utilize centroid of non-dominated solutions found through Multi-objective Optimization with Evolutionary Algorithm (MOEA) from previous environmental change. This centroid will become part of MOEA's initial population to find the solutions for the current change. The first part of our paper deals mainly on the extension of CBA, called Mapping Task IDs for CBA (McBA), to solve problems resulting from time-varying number of tasks. This second part will show the versatility of McBA over a portfolio of algorithms with respect to the degree of changes in environment. This demonstration was accomplished by finding a model relating the degree of changes to the performance of McBA using Nonlinear Principal Component Analysis. From this model, the degree of change at which McBA's performance becomes unacceptable can be found. Results showed that McBA, and its variant called Random McBA, can withstand larger environmental changes than those of other algorithms in the portfolio.