Blast furnaces play a critical role in the steel industry, and their operational optimization is crucial for energy conservation and emissions reduction. This paper examines the impact of changes in operational conditions on blast furnace performance. We propose a dynamic multi-objective optimization algorithm based on multiple short time series (MT-DC-RVEA) to solve the constructed dynamic multi-objective operational optimization model for blast furnaces. Experimental results validate the effectiveness of the proposed algorithm in solving the operational optimization model for blast furnace operations.
Under the condition of informatized joint combat system, the traditional target value analysis method based on human judgment is no longer able to quickly and accurately judge the target value from the massive battlefield information. In this paper, combining with the attention mechanism model, we propose a dual-driven target value assessment framework based on target and data, and construct data-driven and purpose-driven attention mechanism models respectively. By selecting multiple features and calculating the saliency of cue targets, the results of the two models are fused to screen out the noticed cue targets and further expanded to form a high-value target set. The experimental results show that the proposed target value assessment model based on attention mechanism is effective, and the matching rate between the actual strike targets and the high-value targets obtained by the model reaches 92%, which is in line with the actual situation of combat and can reflect the cognitive process of the commander to some extent.
Membrane computing provides efficient computing devices for broad applications due to its distributed storage and the parallel processing. As computing devices in membrane computing, numerical P systems with thresholds (NPT systems) are proven to be Turing universal, and their computational and operational semantics need to be further investigated. In this work, the intrinsic relationship between NPT systems and Petri nets is concerned. The ingredients of Petri nets are associated with the elements of numerical variables in NPT systems, and the operations of Petri nets are associated with the evolutions of NPT systems. The results on the boundedness and reachability of NPT systems are obtained by using the relationship between NPT systems and Petri nets.
For the latest two years, relation classification-based surrogate-assisted algorithms show good potential for solving expensive multi-objective optimization problems (EMOPs). In this category of methods, the used dominance relation that is vital for building training dataset and selecting promising solutions to reduce expensive real function evaluations (FEs). However, the existing studies are still at the initial stage and lack specific research on the dominance relation. This paper proposes a novel dominance relation called Difference Vector Angle Dominance with an angle threshold for EMOPs (called as DVAD- φ ). The proposed DVAD- φ has adaptive selection pressure and considers the convergence and diversity of solutions when picking out superior solutions, which makes it beneficial to pick out promising solutions for expensive real FEs and reduce expensive real FEs. To be specific, we firstly give the definition of DVAD- φ that measures the superiority from one solution to another solution, where the angle threshold φ controls the selection pressure. Then, we propose an adaptive determination strategy of angle threshold based on bisection to set proper pressure for picking out promising solutions for expensive real FEs. Experiments have been conducted on 7 test functions from one benchmark set. The experimental results have verified the effectiveness of DVAD- φ .
Global search is a fundamental task in optimization, aiming to find the optimal solution across the entire search space. To address the challenges in global search, a hierarchical competitive differential evolution algorithm is proposed. It uniquely incorporates a hierarchical competition mechanism and an adaptive differential mutation strategy based on competition outcomes, substantially enhancing global search. The proposed algorithm benchmarks on a total of 30 international test functions of CEC 2017 benchmark functions. The convergence accuracy, coupled with the outcomes of two nonparametric statistical tests, the Friedman test and the Wilcoxon signed-rank test, clearly demonstrates that HCDE exhibits competitive performance when compared to the other 14 efficient optimizers.
