Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite

TL;DR

This paper presents a comprehensive framework for creating realistic and challenging benchmarks for dynamic multi-objective optimization, incorporating novel features like changing Pareto sets, variable interactions, and temporal perturbations.

Contribution

It introduces a generalized benchmark framework with innovative components to better simulate real-world dynamic multi-objective problems for algorithm evaluation.

Findings

The framework produces more realistic and complex test problems.

It outperforms traditional benchmarks in discriminating algorithm performance.

Experimental results validate the framework's effectiveness and superiority.

Abstract

Dynamic multi-objective optimization (DMOO) has recently attracted increasing interest from both academic researchers and engineering practitioners, as numerous real-world applications that evolve over time can be naturally formulated as dynamic multi-objective optimization problems (DMOPs). This growing trend necessitates advanced benchmarks for the rigorous evaluation of optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework features several novel components: 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. This work establishes a new standard for dynamic multi-objective optimization benchmarking, providing a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems.