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.
Differential Evolution (DE), a population-driven stochastic optimization technique, has garnered significant interest among researchers across diverse disciplines because of its simple approach, high resilience, and few control parameters. However, numerous existing DE variants frequently encounter limitations when tackling intricate optimization problems, especially due to premature convergence weakness. To mitigate these deficiencies, the paper proposes an adaptive differential evolution with a deeply informed mutation strategy and historical information for numerical optimization (ADEDH), the main contributions of which can be outlined as follows: Firstly, a bi-stage parameter control strategy is proposed to achieve a better balance between exploration and exploitation. Secondly, a deeply informed mutation strategy is implemented, which uses the historical population to mirror the objective landscape and help guide the evolution. Thirdly, a diversity enhancement strategy based on historical information is proposed to tackle the premature convergence weakness. ADEDH is evaluated against nine outstanding competitors under a vast testing framework, containing CEC2013, CEC2014, and CEC2017 test suites. Additionally, the feasibility of ADEDH is further validated through its application to the parameter identification problem of a photovoltaic model. Experimental results demonstrate that ADEDH diversifies the population, attains superior solution precision, and achieves better stability.
Substituted MnAs0.97P0.03 manganese arsenide is prepared, and its structural, magnetic, and magnetocaloric properties are studied. At 260 K, an abrupt decrease in the magnetization of the sample is observed, which is interpreted as a ferromagnetic–paramagnetic transition. The magnetostructural phase transition is accompanied by the magnetocaloric effect, which is observed in magnetic fields up to 13.5 T.
A practical hybrid flow shop scheduling problem under uncertain processing stages, which derives from the steelmaking continuous casting (SCC) process, is investigated in this paper. Firstly, a mixed-integer mathematical model of the SCC scheduling problem with uncertain processing stages (SCCSPUPS) is established, and the feature of which is analyzed. Secondly, a novel heuristic approach based on the feature of uncertain processing stages is designed, and the better solution can be obtained. Thirdly, a two-dimensional discrete artificial bee colony algorithm based on multiple neighborhood swaps (DABCMNS) is proposed to solve this SCCSPUPS, where a two-dimensional encoding scheme is developed for the solution representation, a four-dimensional tournament selection strategy is explored to select, and the multiple neighborhood swaps operator is designed to conduct the neighborhood searches for the better individuals. Finally, a comparison of DABCMNS algorithm with other six notable metaheuristic algorithms is carried out, and the findings indicate that the DABCMNS algorithm exhibits superior performance in comparison to other metaheuristic methods.
We derive the Dirac equation in the background of the Newman-Unti-Tamburino (NUT) spacetime by applying the tetrad formalism, and separate the angular and radial parts. We get the system of two differential equations for angular functions and solve them in terms of hypergeometric functions. Then a NUT-charge dependent quantization rule for the angular separation constant has been established. As a result of studying the radial equations, we demonstrate that the probability of particle-antiparticle production on the outer event horizon decreases with the increase of the NUT charge. For the massless fermion, we construct the solution of the radial system of Dirac equation in terms of the confluent Heun functions that allows to get the NUT-charge dependent scattering resonances. Under the assumption of small NUT charge, we study the extremal NUT black hole with a single horizon, when the Bekenstein-Hawking entropy vanishes identically, and reveal the non-zero NUT charge effects in wave characteristics.
