Benchmarking Continuous Dynamic Multi-Objective Optimization: Survey and Generalized Test Suite
Chang Shao, Qi Zhao, Nana Pu, Shi Cheng, Jing Jiang, and Yuhui Shi

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…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Metaheuristic Optimization Algorithms Research · Advanced Optimization Algorithms Research
