Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite
For researchers in dynamic multi-objective optimization, this work provides a more realistic and challenging test suite to evaluate algorithm performance, addressing the need for benchmarks that better reflect real-world dynamic systems.
This paper introduces a generalized framework for constructing realistic dynamic multi-objective optimization benchmarks, incorporating novel components like hypersurface Pareto-optimal set changes, variable contribution imbalances, dynamic rotations, temporal perturbations, and time-linkage mechanisms. The framework demonstrates superiority over conventional benchmarks in realism, complexity, and discriminative power for state-of-the-art algorithms.
The field of Dynamic Multi-Objective Optimization (DMOO) has witnessed a surge of interest from both academia and industry, as numerous time-evolving real-world applications can be naturally formulated as Dynamic Multi-Objective Optimization Problems (DMOPs). This growing demand thus necessitates advanced benchmarks to rigorously evaluate optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework incorporates several novel components, including: a generalized formulation that allows the Pareto-optimal Set (PS) to change on hypersurfaces; a mechanism for creating controlled variable contribution imbalances to generate heterogeneous landscapes; and dynamic rotation matrices for inducing time-varying variable interactions and non-separability. Furthermore, we incorporate a temporal perturbation mechanism to simulate irregular environmental changes and propose a generalized time-linkage mechanism that systematically embeds historical solution quality into future problems, thereby capturing critical real-world phenomena such as error accumulation and time-deception. Extensive experimental results validate the effectiveness of the proposed framework, demonstrating its superiority over conventional benchmarks in terms of realism, complexity, and its capability for discriminating state-of-the-art algorithmic performance. Thus, this work establishes a new standard for dynamic multi-objective optimization benchmarking and provides a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems.