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Abstract Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

The rapid development of 5G/6G and AI enables an environment of Internet of Everything (IoE) which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay. However, these massive devices will lead to explosive traffic growth, which in turn cause great burden for the data transmission and content delivery. This challenge can be eased by sinking some critical content from cloud to edge. In this case, how to determine the critical content, where to sink and how to access the content correctly and efficiently become new challenges. This work focuses on establishing a highly efficient content delivery framework in the IoE environment. In particular, the IoE environment is re-constructed as an end-edge-cloud collaborative system, in which the concept of digital twin is applied to promote the collaboration. Based on the digital asset obtained by digital twin from end users, a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention (TPA) enabled Long Short-Term Memory (LSTM) model. Then, the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning (RL) technology. Finally, a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead. The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.

Given the abundance of potassium resources, potassium-ion batteries are considered a low-cost alternative to lithium-ion types. However, their electrochemical performance remains rather unsatisfactory because potassium ions have sluggish kinetics and large ionic radius. In this study, NiCo2Se4 nanotube spheres are synthesized as efficient potassium storage hosts via a facile two-step hydrothermal process. The rationally designed electrode has various ameliorating morphological and functional features, including the following: (i) A hollow structure allows for relief of the volume expansion while offering an excellent electrochemical reactivity to accelerate the conversion kinetics; (ii) a high electrical conductivity for enhanced electron transfer; and (iii) myriad vacancies to supply active sites for electrochemical reactions. As such, the electrode delivers an initial reversible capacity of 458.1 mAh g−1 and retains 346.6 mAh g−1 after 300 cycles at 0.03 A g−1. The electrode sustains a high capacity of 101.4 mAh g−1 even at a high current density of 5 A g−1 and outperforms the majority of state-of-the-art anodes in terms of both cyclic capacity and rate capability, especially at above 1.0 A g−1. This study not only proves bimetallic selenides are promising candidates for potassium storage devices but also offers new insight into the rational design of electrode materials for high-rate potassium-ion batteries.

Today, many-objective optimization problems have attracted widespread attention. There are significant advantages of the grid-based algorithm in solving multi-objective problems. Grid-based algorithm could offer a transformation of objectives and further distinguish the non-dominated solutions. However, the advantages of grid have not been fully exploited. For example, the traditional homogeneous grid divisions can’t sufficiently reveal the similarity of adjacent solutions. And overemphasizing the selection pressure may cause the diversity decline of grid. To exploit the potentialities of grid, an inhomogeneous grid-based evolutionary algorithm (named IGEA) is proposed. IGEA applies a dynamic inhomogeneous grid division approach and redefining the coordinate assignment of individuals, which makes the dominance relationship more obvious. IGEA also applied the shift-based density estimation (SDE) strategy in discriminating the non-dominated solutions in grid coordinate. SDE can provide a good balance of convergence and diversity. The IGEA compares with several state-of-the-art evolutionary algorithms against the regular and irregular many-objective optimization problems. The experimental results demonstrate that IGEA is very competitive against the peer algorithms in terms of providing a good balance between convergence and diversity.

Abstract Phosphorus‐ and phosphide‐based materials with remarkable physicochemical properties and low costs have attracted significant attention as the anodes of alkali metal (e.g., Li, Na, K, Mg, Ca)‐ion batteries (AIBs). However, the low electrical conductivity and large volume expansion of these materials during electrochemical reactions inhibit their practical applications. To solve these problems, various promising solutions have been explored and utilized. In this review, the recent progress in AIBs using phosphorus‐ and phosphide‐based materials is summarized. Thereafter, the in‐depth working principles of diverse AIBs are discussed and predicted. Representative works with design concepts, construction approaches, engineering strategies, special functions, and electrochemical results are listed and discussed in detail. Finally, the existing challenges and issues are concluded and analyzed, and future perspectives and research directions are given. This review can provide new guidance for the future design and practical applications of phosphorus‐ and phosphide‐based materials used in AIBs.

The cache allocation of in-network caching is fundamental to the quality of service in heterogeneous information centric network (ICN). The aim of the cache allocation is to efficiently allocate appropriate cache capacity to each router for storing content, avoiding the long-distance transmission cost from clients to servers. Existing works focus on the analysis of network topology. However, this way is computation-expensive for such analysis, at the same time it cannot reach a completely optimal network, due to its used multiple conflicting performance metrics. In this paper, we propose an ant colony inspired cache allocation mechanism (ACCM) for heterogeneous ICN, which is able to deal with the cache budget constraint. Specifically, we first construct an evaluation model of network performance, in terms of hit ratio, energy consumption, time latency, and throughput. Then, based on the evaluation model, we devise an ant colony inspired cache allocation mechanism, where the ICN topology system is mapped into an ant colony system and the cache allocation process is simulated by the ant colony foraging behavior. Our theoretical analysis shows that the proposed mechanism can converge to the best solution. Finally, we conduct simulations on AttMpls and TW Telecom topologies with real datasets in YouTube. Simulation results show the effectiveness and efficiency of the proposed mechanism on the simulation instances in terms of hit ratio, energy consumption, time latency, and throughput.

With the continuous development of E-commerce, warehouse logistics is also facing emerging challenges, including more batches of orders and shorter order processing cycles. When more orders need to be processed simultaneously, some existing task scheduling methods may not be able to give a suitable plan, which delays order processing and reduces the efficiency of the warehouse. Therefore, the intelligent warehouse system that uses autonomous robots for automated storage and intelligent order scheduling is becoming mainstream. Based on this concept, we propose a multi-robot cooperative scheduling system in the intelligent warehouse. The aim of the multi-robot cooperative scheduling system of the intelligent storage is to drive many robots in an intelligent warehouse to perform the distributed tasks in an optimal (e.g., time-saving and energy-conserved) way. In this paper, we propose a multi-robot cooperative task scheduling model in the intelligent warehouse. For this model, we design a maximin-based multi-objective algorithm, which uses a one-by-one update scheme to select individuals. In this algorithm, two indicators are devised to discriminate the equivalent individuals with the same maximin fitness value in the environmental selection process. The results on benchmark test suite show that our algorithm is indeed a useful optimizer. Then it is applied to settle the multi-robot scheduling problem in the intelligence warehouse. Simulation experiment results demonstrate the efficiency of the proposed algorithm on the real-world scheduling problem.

Surrogate-assisted evolutionary algorithms have received a surge of attentions for their promising ability of solving expensive optimization problems. Existing surrogate-assisted evolutionary algorithms usually adopt the regression models and the binary classification models to guide the evolution of the population for solving the multiobjective optimization problems. However, the regression models will make the algorithm to be increasingly computation-expensive as the number of objectives increases, while the use of the binary classification models might suffer from the poor diversity since the diversified information of solutions cannot be reflected in these classification models. For this issue, this paper proposes a surrogate-assisted many-objective evolutionary algorithm using the cooperation of the multi-classification and regression models to improve the search quality while reducing the computational cost. Our approach includes two parts: At the model training stage, a multi-classification model is constructed to divide the whole population into several classes for ensuring diversity, a distance regression model and an angle regression model are used to select solutions with better convergence and diversity in each class; At the evolution stage, a coevolutionary framework is used to guide the evolution according to a new selection criterion. Experimental results verify the effectiveness of the proposed algorithm on a set of expensive test problems with up to 10 objectives.

Abstract Functional carbonaceous materials for supercapacitors (SCs) without using acid for post-treatment remain a substantial challenge. In this paper, we present a less harmful strategy for preparing three-dimensional (3D) N,O-codoped egg-box-like carbons (EBCs). The as-prepared EBCs with opened pores provide plentiful channels for ion fast transport, ensure the effective contact of EBCs electrodes and electrolytes, and enhance the electron conduction. The nitrogen and oxygen atoms doped in EBCs improve the surface wettability of EBC electrodes and provide the pseudocapacitance. Consequently, the EBCs display a prominent areal capacitance of 39.8 μF cm−2 (340 F g−1) at 0.106 mA cm−2 in 6 M KOH electrolyte. The EBC-based symmetric SC manifests a high areal capacitance to 27.6 μF cm−2 (236 F g−1) at 0.1075 mA cm−2, a good rate capability of 18.8 μF cm−2 (160 F g−1) at 215 mA cm−2 and a long-term cycle stability with only 1.9% decay after 50,000 cycles in aqueous electrolyte. Impressively, even in all-solid-state SC, EBC electrode shows a high areal capacitance of 25.0 μF cm−2 (214 F g−1) and energy density of 0.0233 mWh cm−2. This work provides an acid-free process to prepare electrode materials from industrial by-products for advanced energy storage devices.

Redox flow batteries are promising for large-scale energy storage, but are hindered by cost, stability, and safety issues. Here the authors construct an all-polymer particulate slurry battery to bypass solubility limits and apply insoluble redox-active materials.

In this work, a new plant-inspired optimization algorithm namely the hybrid artificial root foraging optimizion (HARFO) is proposed, which mimics the iterative root foraging behaviors for complex optimization. In HARFO model, two innovative strategies were developed: one is the root-to-root communication strategy, which enables the individual exchange information with each other in different efficient topologies that can essentially improve the exploration ability; the other is co-evolution strategy, which can structure the hierarchical spatial population driven by evolutionary pressure of multiple sub-populations that ensure the diversity of root population to be well maintained. The proposed algorithm is benchmarked against four classical evolutionary algorithms on well-designed test function suites including both classical and composition test functions. Through the rigorous performance analysis that of all these tests highlight the significant performance improvement, and the comparative results show the superiority of the proposed algorithm.

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.