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In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.Keywords Change severity sensing · Dynamic multiobjective optimization · Time-changing objective functions · Adaptive population management · Memory-based strategy · Local search-based strategy · Change severity-based strategy

Dynamic Multi-objective Optimization (DMO) is a challenging research topic since the objective functions, constraints, and problem parameters may change over time. Several evolutionary algorithms have been proposed to deal with DMO problems. Nevertheless, they were restricted to unconstrained or domain constrained problems. In this work, we focus on the dynamicty of problem constraints along with time-varying objective functions. As this is a very recent research area, we have observed a lack of benchmarks that simultaneously take into account these characteristics. To fill this gap, we propose a set of test problems that extend a suite of static constrained multi-objective problems. Moreover, we propose a new version of the Dynamic Non dominated Sorting Genetic Algorithm II to deal with dynamic constraints by replacing the used constraint-handling mechanism by a more elaborated and self-adaptive penalty function. Empirical results show that our proposal is able to: (1) handle dynamic environments and track the changing Pareto front and (2) handle infeasible solutions in an effective and efficient manner which allows avoiding premature convergence. Moreover, the statistical analysis of the obtained results emphasize the advantages of our proposal over the original algorithm on both aspects of convergence and diversity on most test problems.

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