An empirical study on the specification of the local search application probability in multiobjective memetic algorithms

This paper empirically examines the effect of the specification of the local search application probability on the performance of multiobjective memetic algorithms. In each generation of multiobjective memetic algorithms, local search is probabilistically applied to each solution. We handle the local search application probability as a controllable parameter. In computational experiments in this paper, we examine the effect of dynamically changing the probability using the five control strategies: constant, step-wise increase, step-wise decrease, linear increase, and linear decrease. Better results are obtained for almost all test problems by changing the local search application probability than specifying it as a constant value. An interesting observation is that the choice of an appropriate control strategy is problem-dependent. Gradually decreasing its value leads to good results for many problems. Such a control strategy, however, does not work on some test problems.