Performance evaluation of sensor-based detection schemes on dynamic optimization problems

Most of the real world optimization problems in different domains demonstrate dynamic behavior, which can be in the form of changes in the objective function, problem parameters and/or constraints for different time periods. Detecting the points in time where a change occurs in the landscape is a critical issue for a large number of evolutionary dynamic optimization techniques in the literature. In this paper, we present an empirical study whose focus is the performance evaluation of various sensor-based detection schemes by using two well known dynamic optimization problems, which are moving peaks benchmark (MPB) and dynamic knapsack problem (DKP). Our experimental evaluation by using two dynamic optimization problem validates the sensor-based detection schemes considered, where the effectiveness of each scheme is measured with the average rate of correctly identified changes and the average number of sensors invoked to detect a change.