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Dynamic hybrid adaptive differential evolution algorithm
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DOI：10.1016/j.eswa.2026.133384.png)
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Abstract
En 中文
Differential Evolution (DE) has established itself as a leading population-based stochastic optimization technique, widely acclaimed for its conceptual simplicity and effectiveness in handling continuous real-parameter problems. Notwithstanding its broad applicability, the canonical DE framework and many refined variants continue to grapple with two longstanding issues: premature convergence and erosion of population diversity, which become particularly pronounced in complex, multi-modal optimization scenarios. These shortcomings often originate from a suboptimal trade-off between exploratory and exploitative behaviors, ineffective mutation operators during stagnant phases, and diversity preservation approaches that tend to disrupt promising search trajectories. To mitigate these limitations, we present the Dynamic Hybrid Adaptive Differential Evolution (DMADE) algorithm, which introduces three principal innovations: first, a dual-phase parameter adaptation mechanism that employs Gaussian-inspired base components and adaptive perturbations to dynamically regulate exploration-exploitation balance; second, a centroid-driven mutation strategy that utilizes population distribution features to rejuvenate trapped solutions; and third, a diversity enhancement technique grounded in potential energy theory, incorporating dynamic interaction sensing and gradient-based relocation. Empirical studies conducted on the CEC2014, CEC2017, and CEC2022 test suites indicate that DMADE consistently outperforms state-of-the-art DE algorithms in terms of solution precision, convergence rate, and algorithmic stability. The method's robustness is further substantiated through extensive statistical testing and component-wise ablation experiments, affirming its efficacy in overcoming core challenges prevalent in contemporary evolutionary optimization.
Keywords:
Differential evolution
Parameter control
Population diversity
Mutation strategy
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