A New Prediction-Based Algorithm for Dynamic Multi-objective Optimization Problems

Abstract

The mechanism for reacting to the changes in an environ- ment when detected is the key issue that distinguishes various algorithms

proposed for dynamic multi-objective optimization problems (DMOPs). The severity of change is a significant approach to identify the dynamic characteristics of DMOPs. In this paper, a prediction-based strategy

based on utilizing the degree of the changes is presented to address envi- ronmental changes. In case of a change detection in the given DMOP, the

severity of change is evaluated and an appropriate reaction mechanism is followed based on the degree of the observed change. To accelerate the convergence process, the algorithm may respond multiple times for

the same change. The performance of our algorithm is evaluated by com- paring it with dynamic multi-objective evolutionary algorithms using six

benchmarks. The effectiveness of our algorithm is demonstrated in the experimental study where it outperforms other compared algorithms in most of the tested instances considered.