Combining a hybrid prediction strategy and a mutation strategy for dynamic multiobjective optimization

The environments of the dynamic multiobjective optimization problems (DMOPs), such as Pareto optimal front (POF) or set (POS), usually frequently change with evolution process. This kind problem poses a higher challenge for evolutionary algorithms because it requires population to quickly track (i.e., converge) position new environment and be widely distributed in search space. prediction-based response mechanism is commonly used method deal environmental changes, but it’s only suitable predictable changes. Moreover, imbalance diversity convergence process tracking dynamically changing POF has aggravated. In this paper, we proposed that combines hybrid prediction strategy precision controllable mutation (HPPCM) solve DMOPs. Specifically, coordinates center point-based guiding individual-based make accurate predictions. Thus, can adapt Additionally, handles unpredictable It improves exploration by controlling variation degree solutions. way, our various changes DMOPs, paper integrates HPPCM into prevalent regularity model-based estimation distribution algorithm (RM-MEDA) optimize results comparative experiments some state-of-the-art on test instances have demonstrated effectiveness competitiveness paper.