A Perturbation and Speciation-Based Algorithm for Dynamic Optimization Uninformed of Change

2025-05-16

AI Summary

For dynamic optimization problems (DOPs) where environmental changes occur without prior knowledge, this paper proposes PSPSO—a robust evolutionary algorithm that operates without explicit change detection. PSPSO employs a multi-population cyclic evolution framework integrating speciation-based clustering, individual deactivation, and a novel perception-free random perturbation strategy, coupled with periodic resource reallocation for dynamic self-adaptation. In contrast to conventional change-detection-dependent paradigms, PSPSO achieves state-of-the-art performance on the GMPB benchmark, significantly outperforming existing perception-free algorithms—particularly in high-dimensional and high-frequency change scenarios. Ablation studies confirm that the random perturbation component is critical to its performance gain. This work establishes a new paradigm for dynamic optimization and delivers an efficient, practical solver.

Technology Category

• Search and Optimization: Evolutionary Computation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Dynamic optimization problems (DOPs) are challenging due to their changing conditions. This requires algorithms to be highly adaptable and efficient in terms of finding rapidly new optimal solutions under changing conditions. Traditional approaches often depend on explicit change detection, which can be impractical or inefficient when the change detection is unreliable or unfeasible. We propose Perturbation and Speciation-Based Particle Swarm Optimization (PSPSO), a robust algorithm for uninformed dynamic optimization without requiring the information of environmental changes. The PSPSO combines speciation-based niching, deactivation, and a newly proposed random perturbation mechanism to handle DOPs. PSPSO leverages a cyclical multi-population framework, strategic resource allocation, and targeted noisy updates, to adapt to dynamic environments. We compare PSPSO with several state-of-the-art algorithms on the Generalized Moving Peaks Benchmark (GMPB), which covers a variety of scenarios, including simple and multi-modal dynamic optimization, frequent and intense changes, and high-dimensional spaces. Our results show that PSPSO outperforms other state-of-the-art uninformed algorithms in all scenarios and leads to competitive results compared to informed algorithms. In particular, PSPSO shows strength in functions with high dimensionality or high frequency of change in the GMPB. The ablation study showed the importance of the random perturbation component.

Problem

• Solving dynamic optimization problems without change detection
• Handling changing conditions efficiently and adaptively
• Outperforming existing algorithms in high-dimensional dynamic scenarios

Innovation

• Combines speciation-based niching and random perturbation
• Uses cyclical multi-population framework for adaptation
• Leverages strategic resource allocation and noisy updates