In the context of green manufacturing, energy consumption issues have attracted widespread attention from all walks of life, especially in the manufacturing industry. In actual industrial production, the distributed blocking flow shop scheduling problem is a typical manufacturing production scenario, but its energy consumption problem has not been effectively solved. In this study, a two-stage cooperative discrete differential evolution with Q-learning (QTCDDE) is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with total energy consumption (TEC) and total tardiness (TTD). An initialization strategy that considers both TEC and TTD is proposed to obtain an initial population with rich search space. In the first stage, a discrete differential evolution is designed to improve the quality of the solution. In the second stage, three types of local search operators are designed, and they are adaptively selected based on historical information and Q-learning. In addition, during the iterative process, the two stages cooperate and complement each other. Finally, each strategy of QTCDDE is effectively verified. Furthermore, QTCDDE is compared with state-of-the-art algorithms in the benchmark suite. Experimental results show that QTCDDE significantly outperforms state-of-the-art algorithms at the 95% confidence interval and effectively solves EEDBFSP.

In the field of message-passing interface (MPI) program path coverage test case generation, evolutionary algorithms (EAs) have been frequently utilized to generate test cases. However, relying solely on EAs will incur excessive computational costs. In this article, we improve the efficiency and quality of MPI program path coverage test cases generated by EAs based on elite individual selection. First, data within the data domain is sampled and fitness is calculated to form a shared set. Then, the population data is initialized using EAs, and the fitness of individuals is predicted using the neighbor value sharing algorithm (NVSA). Subsequently, individuals are ranked using rank-based elite selection (RES). Finally, elite individuals are chosen through ranking to run the program and verify the generation of test cases. In order to reduce computational costs, data dimensionality reduction operations are added to the above process. We demonstrate that the proposed method can effectively generate test data and reduce test costs by comparing it with several excellent methods on seven representative MPI programs. Among them, NVSA has a maximum improvement of 42.2%, RES has a maximum improvement of 31.5%, dimensionality reduction can increase by 20.2%, and the overall method has a maximum improvement of 47.4%.

A target path of Message Passing Interface (MPI) programs typically consists of several target sub-paths. During solving a test case that cover the target path using an intelligent optimization algorithm, we often find that there are some hard-to-cover target sub-paths, which limit the testing efficiency of the entire target path. Therefore, this paper proposes an approach of low-cost testing for path coverage of MPI programs using surrogate-assisted changeable multi-objective optimization, which is used to further improve the effectiveness and efficiency of test case generation. The proposed approach first establishes a changeable multi-objective optimization model, which is used to guide the generation of test cases. During solving the changeable multi-objective optimization model using an intelligent optimization algorithm, we then determine each hard-to-cover target sub-path and form a corresponding sample set. Finally, we manage the surrogate model corresponding to each hard-to-cover target sub-path based on the formed sample set, and select superior evolutionary individuals to really execute the MPI program under test, thus reducing the cost and times of program execution. The proposed approach has been applied to path coverage testing of several benchmark MPI programs, and compared with several state-of-the-art approaches. The experimental results show that the proposed approach significantly improves the effectiveness and efficiency of generating test cases.

Realistic 3D full-body talking avatars hold great potential in AR, with applications ranging from e-commerce live streaming to holographic communication. Despite advances in 3D Gaussian Splatting (3DGS) for lifelike avatar creation, existing methods struggle with fine-grained control of facial expressions and body movements in full-body talking tasks. Additionally, they often lack sufficient details and cannot run in real-time on mobile devices. We present TaoAvatar, a high-fidelity, lightweight, 3DGS-based full-body talking avatar driven by various signals. Our approach starts by creating a personalized clothed human parametric template that binds Gaussians to represent appearances. We then pre-train a Style-Unet-based network to handle complex pose-dependent non-rigid deformation, which can capture high-frequency appearance details but is too resource-intensive for mobile devices. To overcome this, we "bake" the non-rigid deformations into a lightweight MLP-based network using a distillation technique and develop blend shapes to compensate for details. Extensive experiments show that TaoAvatar achieves state-of-the-art rendering quality while running in real-time across various devices, maintaining 90 FPS on high-definition stereo devices such as the Apple Vision Pro.

The most obvious characteristic of dynamic multiobjective optimization problems (DMOPs) is the time-varying Pareto-optimal set (POS) or/and Pareto-optimal front (POF). This kind of problem poses a higher challenge to the evolutionary algorithms, as it requires populations to rapidly track and converge the updated POF in new environments. Differing from the superposition of several strategies in the literatures, we propose an adaptive multistrategy algorithm based on the extent of environmental change, called AMEEC to effectively handle various dynamic changes. AMEEC chooses the corresponding strategies for different environmental changes adaptively. When the environment changes moderately or similarly, the prediction based on the clustered center points, POS manifold prediction, and generation of random solutions based on the ideal points are employed to relocate the population individuals in the new environment. Otherwise, the trend prediction model is employed to predict the knee points of each part and the center points of each cluster, and to adaptively adjust the area of random solutions based on the ideal and nadir points focuses on enhancing the diversity of population members. The proposed AMEEC is tested comprehensively on 19 benchmark problems compared with the six state-of-the-art algorithms. All algorithms use RM-MEDA (a regularity model-based multiobjective estimation of distribution algorithm) as a static optimizer. The experimental results demonstrate that AMEEC can achieve good convergence, diversity, and distribution, and is more competitive in dealing with the dynamic problems.

Dynamic interval multiobjective optimization problems, such as those encountered in wireless sensor network scheduling and portfolio selection, are increasingly prevalent. However, they present significant challenges due to the inherent uncertainty and variability of their parameters. This article proposes a dynamic interval multiobjective evolutionary algorithm in terms of multitask learning and inverse mapping. The algorithm employs an interval-based MOEA/D as its foundational framework. Upon encountering environmental changes, prediction models for the midpoints and widths of intervals are developed using multitask learning combined with a self-evolving fuzzy system. These models generate a predicted Pareto front (PF) in the objective space. Subsequently, inverse mappings of the objective functions at the new time are established to identify solutions that correspond to the predicted PF in the decision space. Finally, these solutions are expanded to form an initial population at the new time. Testing on 18 benchmark optimization problems and an uncertain scheduling of underwater wireless sensor networks, the proposed algorithm demonstrates both competitiveness and superiority when compared to five state-of-the-art algorithms.

This article studies an energy-efficient hybrid flow-shop scheduling problem (EHFSP) considering variable machine speed (EHFSP-VMS). To minimize the makespan and total energy consumption simultaneously, a tree-based multiobjective evolutionary algorithm (TMOEA) is proposed. It decomposes a multiobjective optimization problem into multiple scalar optimization subproblems and optimizes them simultaneously. Moreover, different from existing multiobjective evolutionary algorithms (MOEAs), TMOEA maintains Pareto archive using a tree structure instead of a linear structure, which reduces the time complexity of updating Pareto solutions from O(P) to O(logP), where P is the size of Pareto archive. In TMOEA, the population evolution is performed in three stages. Stage one is responsible for local search, in which five different operators are combined in the form of a roulette wheel to explore promising solutions in neighboring regions. Stage two is responsible for global search, which uses a technique for order preference by similarity to an ideal solution (TOPSIS) to select promising solutions for crossover to promote the population to evolve toward the Pareto front. Stage three is responsible for preventing local optimality, where a neighbor exchange method and opposition-based learning strategy are adopted to escape the local optimal. Computational experiments demonstrate that the tree structure is effective and TMOEA is superior to the existing algorithms in solving EHFSP-VMS.

In recent years, as the demand for computing power in scientific computing has progressively increased, thfige scale of scientific applications has also grown, at the same time the number of cloud platform tenants is also expanding dramatically, cloud platforms often need to handle multiple large-scale heterogeneous workflows at the same time. Moreover, the rapid increment in the scale and performance of cloud computing centers has brought about even greater energy consumption, a large number of studies for multi-objective task scheduling usually focus on cost and makespan as the optimization objectives but often neglect the optimization of energy consumption. In order to solve the above problems, a shortest workflow first adaptive fast nondominated sorting genetic algorithm (SWFAGA) is proposed. First, a shortest workflow first workflows combinatorial encoding method is proposed which can encode multiple workflows as chromosomes based on the heterogeneity of workflows. Second, an adaptive crossover strategy based on Q-learning to improve the convergence speed without adding extra time overhead. In addition, a reference point-based convergence calculation method is devised. Finally, through the heterogeneous large-scale real-world multi-workflow, SWFAGA is verified to outperform state-of-the-art multi-objective optimization scheduling algorithms in terms of operational efficiency, stability, convergence, and diversity.

Peak carbon emissions and carbon neutrality have become important initiatives for the country to solve outstanding problems of resource and environmental constraints and promote green and low-energy development, and have attracted widespread attention from the industry. The distributed flow shop scheduling problem (DPFSP) is a typical problem that mainly works by consuming energy. However, DPFSP rarely considers energy efficiency and blocking constraints. In this study, an excellent bi-population cooperative discrete differential evolution (BCDDE) is proposed, aiming to address the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with total energy consumption (TEC) and total tardiness (TTD) as two objectives. A bi-population cooperative strategy is constructed to enhance the diversity of BCDDE, while utilizing it to initialize the population to enhance the quality of the initial solution. An adaptive local search operator strategy is developed to improve the BCDDE convergence. Critical and noncritical paths are devised to further optimize TEC and TTD objectives. The efficiency of each strategy related to BCDDE is verified and compared with state-of-the-art algorithms in the benchmark suite. Numerical results show that BCDDE becomes an efficient optimizer for the EEDBFSP, significantly outperforming the state-of-the-art algorithms at the 95% confidence interval.

Remote Sensing Image Super-Resolution (RSISR) techniques play a crucial role in various remote sensing applications. However, deep learning-based methods applied to RSISR encounter difficulties in learning complex features of remote sensing images and modeling long-term correlations between pixels. This study proposes aMulti-Scale Sliding Window Attention Generation Adversarial Network (MSWAGAN), which combines the advantages of Convolutional Neural Networks (CNN) and Transformers to overcome these limitations. The MSWAGAN consists of three main parts. In the shallow feature extraction part, CNN is used to extract shallow features from remote sensing images. The deep feature extraction part is divided into two stages. Firstly, amulti-scale sliding window attention (MSWA)is designed to replace the multi-head attention (MHA) in the Transformer. MSWA can learn local multi-scale complex features of remote sensing images without increasing the number of parameters in MHA. Then, the Transformer is utilized to learn global image features and model the long-range correlations between pixels. The image reconstruction part utilizes sub-pixel convolution for feature upsampling. Furthermore, in order to extend the application of super-resolution remote sensing images, a cross-sensor real multi-spectral RSISR dataset consisting of Landsat-8 (L8) and Sentinel-2 (S2) images was constructed, and a series of experiments to improve the spatial resolution of L8 images from 30m to 10m in B, G, R and Near Infrared (NIR) bands were conducted. Experimental results demonstrate that our method outperforms some of the latest SR methods.

Message passing interface (MPI) is a powerful tool for parallel computing, originally designed for high-performance computing on massively parallel computers. In this paper, we combine FERPSO-IMPR (fitness Euclidean distance ratio particle swarm optimizer with information migration-based penalty and population reshaping) and surrogate-assisted models to generate test cases for MPI program path coverage testing. In our proposed method, FERPSO-IMPR employs a dual population strategy to initialize data and calculate fitness. Then, we create a sample set based on the initial data and its fitness. Subsequently, we train the master-slave surrogate models to predict individual fitness. Finally, a small number of elite individuals are selected to execute the program to decide whether to generate the required test data and guide the subsequent evolution process. We apply the proposed method to seven MPI programs and perform experimental comparisons from five directions. Experimental results show that compared with the comparative method, the time consumption of the proposed method is reduced by 33.2%, the number of evaluations is reduced by 38.8%, and the success rate is increased by 7.6%. These results prove that our method can effectively reduce the test data generation cost of MPI programs.

Solving dynamic multiobjective optimization problems (DMOPs) is very challenging due to the requirements to respond rapidly and precisely to changes in an environment. Many prediction- and memory-based algorithms have been recently proposed for meeting these requirements. However, much useful knowledge has been ignored during the historical search process, and prediction deviations could occur, thus limiting the applicability of these methods to a variety of problems. Facing these concerns, this article proposes an evolutionary algorithm named FGTTMP based on feedback-guided transfer (FGT) and trend manifold prediction (TMP) for solving DMOPs. The FGT employs an information feedback model to extract valuable knowledge using all historical environments and then identifies excellent individuals using cluster-transfer learning. This can both accelerate convergence and introduce diversity for future environments. The TMP applies the probability-based trend prediction method to estimate the mass center of the whole population and the corresponding manifold, relying on the two previous moments. Thus, the FGT and TMP strategies combine historical knowledge with prediction techniques, synergistically leading the population in promising directions. The performance of the proposed algorithm is fully investigated and compared with eight state-of-the-art algorithms. Experimental results demonstrate that the proposed FGTTMP method can achieve better convergence and diversity on 19 various benchmark problems than the state-of-the-art algorithms.

El Niño/Southern Oscillation (ENSO) mainly occurs in the tropical Pacific Ocean every a few years. But it affects the climate around the world and has a dramatic impact on the development of ecology and agriculture. The analysis and prediction of ENSO become particularly important for meteorology and disaster management. However, due to insufficient data, spring predictability barrier (SPB), and model uncertainty, traditional analysis models face challenges. To address these issues, researchers begin to apply deep learning (DL) technologies to ENSO research, exploring the impact of ENSO on the world's extreme climate changes. In recent years, deep learning-based methods have obtained impressive progress with more accurate and effective predictions of ENSO. In this paper, we summarize the attempts of DL technologies in predicting ENSO. We first introduce the properties of ENSO, followed by the architecture introduction of DL technologies and their application to ENSO. We then investigate the potential of DL technologies for ENSO prediction from various aspects, including model evaluation metrics, prediction algorithms, overcoming SPB and prediction uncertainty. Finally, we provide discussions on the future trends and challenges of using DL technologies for ENSO prediction.

In real-world applications, a specific class of multiobjective optimization problems, such as the cloud service allocation problem (CSAOPs), possess the characteristic of variable-length and mixed variables, termed as variable multiobjective optimization problems (VMMOPs). Unfortunately, little research has been reported to solve them. To fill the gap, we propose a tailored enhanced decomposition-based algorithm to handle the VMMOPs. Specifically, a variable-length coding structure is designed to flexibly represent the solutions of VMMOPs. In order to facilitate the solution generation, a simple dimensionality incremental learning strategy is developed to choose representative solutions for the training of two learning models. The one is the fast-clustering-based histogram model, which is built for the sampling of solutions in the continuous decision space, while the other one is the incremental learning-based histogram model, designed to sample solutions in discrete decision space. Following the traditional constructor of the DTLZ test suite and the features of CSAOPs, we present a test suite of VMMOPs for the verification of the performance of the methods in handling VMMOPs. Experimental results on a number of benchmark problems and two real CSAOPs have shown the effectiveness and competitiveness of the proposed method in handling VMMOPs.

doi:10.1093/comjnl/bxz149 Comp J 2020;00(0): xxxx–xxxx In the originally published version of this manuscript, two corrections were requested and listed in this corrigendum. Upon the original publication, the authors' affiliation were incorrect and have since been updated. Upon the original publication, the conflict of interest section neglected to acknowledge that there are no conflicts of interest. These errors have now been corrected online.

The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR andF) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting ofNparents andNchildren in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization).

Internet of Things assisted healthcare services grants reliable clinical diagnosis and analysis by exploiting heterogeneous communication and infrastructure elements. Communication is enabled through point-to-point or cluster-to-point between the users and the diagnosis center. In this process, the complication is the resource sharing and diagnosis swiftness invalidating multiple resources. IoT's open and ubiquitous nature results in proactive resource sharing, resulting in delayed transmissions. This manuscript introduces the Redemptive Resource Sharing and Allocation (R2SA) scheme to address this issue. The available health data is accumulated on a first-come-first-serve basis, and the transmitting infrastructure is selected. In this process, the data-to-capacity of the available infrastructure is identified for non-redemptive resource allocation. The extremity of the capacity and unavailability of the resource is then analyzed for parallel processing and allocation. Therefore, the data accumulation and exchange rely on concurrent sharing and resource allocation processes, deferring a better accumulation ratio. The concurrent redemptive selection and sharing reduces transmission delay, improves resource allocation, and reduces transmission complexity. The entire process is managed for transfer learning, data-to-capacity validation, and concurrent recommendation. The first validation knowledge base remains the same/shared for different data accumulation and sharing intervals.

Abstract As a novel swarm intelligence optimization algorithm, cuckoo search (CS) has been successfully applied to solve diverse problems in the real world. Despite its efficiency and wide use, CS has some disadvantages, such as premature convergence, easy to fall into local optimum and poor balance between exploitation and exploration. In order to improve the optimization performance of the CS algorithm, a new CS extension with multi-swarms and Q-Learning namely MP-QL-CS is proposed. The step size strategy of the CS algorithm is that an individual fitness value is examined based on a one-step evolution effect of an individual instead of evaluating the step size from the multi-step evolution effect. In the MP-QL-CS algorithm, a step size control strategy is considered as action, which is used to examine the individual multi-stepping evolution effect and learn the individual optimal step size by calculating the Q function value. In this way, the MP-QL-CS algorithm can increase the adaptability of individual evolution, and a good balance between diversity and intensification can be achieved. Comparing the MP-QL-CS algorithm with various CS algorithms, variants of differential evolution (DE) and improved particle swarm optimization (PSO) algorithms, the results demonstrate that the MP-QL-CS algorithm is a competitive swarm algorithm.

One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.

The emergence of fuzzy sets makes job-shop scheduling problem (JSSP) become better aligned with the reality. This article addresses the JSSP with fuzzy execution time and fuzzy completion time (FJSSP). We choose the classic differential evolution (DE) algorithm as the basic optimization framework. The advantage of the DE algorithm is that it uses a special evolutionary strategy of difference vector sets to carry out mutation operation. However, DE is not very effective in solving some instances of FJSSP. Therefore, we propose a novel selection mechanism augmenting the generic DE algorithm (NSODE) to achieve better optimization results. The proposed selection operator adopted in this article aims at a temporary retention of all children generated by the parent generation, and then selecting N better solutions as the new individuals from N parents and N children. Various examples of fuzzy shop scheduling problems are experimented with to test the performance of the improved DE algorithm. The NSODE algorithm is compared with a variety of existing algorithms such as ant colony optimization, particle swarm optimization, and cuckoo search. Experimental results show that the NSODE can obtain superior feasible solutions compared with solutions produced by several algorithms reported in the literature.

In most metaheuristic algorithms, the updating process fails to make use of information available from individuals in previous iterations. If this useful information could be exploited fully and used in the later optimization process, the quality of the succeeding solutions would be improved significantly. This paper presents our method for reusing the valuable information available from previous individuals to guide later search. In our approach, previous useful information was fed back to the updating process. We proposed six information feedback models. In these models, individuals from previous iterations were selected in either a fixed or random manner. Their useful information was incorporated into the updating process. Accordingly, an individual at the current iteration was updated based on the basic algorithm plus some selected previous individuals by using a simple fitness weighting method. By incorporating six different information feedback models into ten metaheuristic algorithms, this approach provided a number of variants of the basic algorithms. We demonstrated experimentally that the variants outperformed the basic algorithms significantly on 14 standard test functions and 10 CEC 2011 real world problems, thereby, establishing the value of the information feedback models.

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

Recently, inspired by the human brainstorming process, a new kind of metaheuristic algorithm, called brain storm optimization (BSO) algorithm was proposed for global optimization. Experimental results have shown its excellent performance when solving optimization problems. In order to further improve the search ability of the BSO, this paper proposes an improved BSO (IBSO) algorithm by introducing graph theory into it. In IBSO, new individuals will be generated to replace some old individuals when the BSO algorithm is in a poor status. Whether a BSO algorithm is in a poor status is determined by the length of Hamiltonian cycle, which can be obtained by transferring all the individuals into an undirected weight graph. A Hamiltonian cycle and its length will be computed according to a modified cycle algorithm. The proposed IBSO algorithm is tested on twelve benchmarks, and the experimental results illustrate its effectiveness.

Bat algorithm (BA) is newly proposed bio-inspired metaheuristic algorithm with the inspiration of the echolocation of bats in nature. Several experimental results have proven to the effectiveness and performance of BA. However, BA may fail to find the global optimal solution occasionally. In this paper, a kind of classical search technology, called variable neighborhood search (VNS), is incorporated into BA as a local search tool. An improved version of BA namely variable neighborhood bat algorithm (VNBA), is thus proposed. In VNBA, the classic BA as a global search tool searches the whole space globally, and this can significantly shrink the search space. Subsequently, VNS as a local search tool is implemented to find the final best solution within the small promising area. After that, the VNBA is benchmarked by sixteen standard benchmark functions. The experimental results imply that VNBA takes the absolute advantage over the basic BA.

Outline-free floorplanning focuses on area and wirelength reductions, which are usually meaningless, since they can hardly satisfy modern design requirements. We concentrate on a more difficult and useful issue, fixed-outline floorplanning. This issue imposes fixed-outline constraints on the outline-free floorplanning, making the physical design more interesting and challenging. The contributions of this paper are primarily twofold. First, a modified simulated annealing (MSA) algorithm is proposed. In the beginning of the evolutionary process, a new attenuation formula is used to decrease the temperature slowly, to enhance MSA’s global searching capacity. After a period of time, the traditional attenuation formula is employed to decrease the temperature rapidly, to maintain MSA’s local searching capacity. Second, an excessive area model is designed to guide MSA to find feasible solutions readily. This can save much time for refining feasible solutions. Additionally, B*-tree representation is known as a very useful method for characterizing floorplanning. Therefore, it is employed to perform a perturbing operation for MSA. Finally, six groups of benchmark instances with different dead spaces and aspect ratios—circuits n10, n30, n50, n100, n200, and n300—are chosen to demonstrate the efficiency of our proposed method on fixed-outline floorplanning. Compared to several existing methods, the proposed method is more efficient in obtaining desirable objective function values associated with the chip area, wirelength, and fixed-outline constraints.

Abstract Digital forensics‐related investigations have been a relative competitive research area by the computer specialists or law enforcement agency officers to confirm or refuse the assumptions with respect to the cybercrime scenes. WeChat is one of the most popular instant messaging application programs in contemporary age. While applying WeChat, there is precious information deposited in the computing devices that could be supportive evidences in a court of law. Hence, this research is focusing on the application of WeChat on the desktop PC under Windows 7 by means of the acquisition of volatile digital evidences in order to provide supporting information for some cybercrime cases. This paper provides generic investigation methods conducting forensic sound analysis on WeChat from the aspect of data volatility of the computing devices. By means of the proposed methodologies, the digital forensics specialists should be able to gather critical digital traces to confirm the previous actions of a WeChat user or even to reconstruct the crime scene to prove the suspect is guilty or innocent in a court of law. Copyright © 2017 John Wiley & Sons, Ltd.

By simulating the echolocation behavior of bats in nature, bat algorithm (BA) is proposed for global optimization that is a recently developed nature-inspired algorithm. Since then, it has been widely used in various fields. Bat algorithm balance the global search and local search by adjusting loudness and pulse rate. However, there is so many loudness and pulse rate combinations that it is hard to choose the most proper one for different problems. In this paper, a multi-swarm algorithm, called multi-swarm bat algorithm (MBA), is proposed for global search problem. In MBA method, immigration operator is used to exchange information between different swarms with different parameter settings. Thus, this configuration can make a good trade-off between global and local search. In addition, the best individuals of every swarm is put into the elite swarm through selection operator. The bat individuals in elite swarm pass over next generation without performing any operators, and this can ensure these best solutions cannot be damaged during optimization process. In order to evaluate the efficiency of MBA method, MBA has been benchmarked by sixteen standard test functions by comparing with basic BA. The results show that the MBA method is able to search more satisfactory function values on most benchmark problems than BA.