Outstanding Algorithms Research Articles

An improved co-evolutionary memetic algorithm based on novel schedule type and unconditional feasibility for hybrid flow-shop scheduling problem

The hybrid flow-shop scheduling problem (HFSP) is used in a wide range of industries and is very hard to solve. There has been a great deal of research on HFSP with the objective of makespan, but related research mainly focused on the algorithmic level, ignoring characters of HFSP. In this paper, through the study of the characters of HFSP itself, together with the design of the algorithm, new records on benchmarks are obtained. Firstly, the property of unconditional feasibility of HFSP (UFH) is formally proven. Based on UFH, the disjunctive-graph-based encoding scheme is considered convenient and accurate to HFSP, and the constraints for feasible neighborhood solutions are superfluous to HFSP. New specialized neighborhood structures are developed for HFSP accordingly. Secondly, traditional schedule types (such as active schedule) are found to be further explored. A novel type of schedule, all-active schedule (AAS) is proposed to tap the potential of one scheduling process. Thirdly, an improved co-evolutionary memetic algorithm (ICMA) based on UFH and AAS is proposed to solve HFSP. To balance exploration and exploitation, the co-evolutionary framework with multiple groups is adopted. Path reconnection is used as a crossover operator, and a non-dominated selection mechanism is also employed. In the end, the effectiveness of ICMA is experimentally demonstrated. 1 new solution on Carlier benchmark (proposed in 2000) and 45 new solutions on Jose benchmark (proposed in 2020) are found by ICMA, outperforming all previous outstanding algorithms.

Real-Time Batch Optimization for the Stochastic Container Relocation Problem

The container relocation problem (CRP) is an important factor affecting the operation efficiency of container terminal yards, and it has attracted much attention for decades. The CRP during the pickup operations of import containers is still an intractable problem for two reasons: the first is that the solution efficiency of the algorithms developed in the existing literature cannot meet the real-time operation requirements; the second is that the pre-optimized operation plan cannot cope with the changes in the real-time operation scenarios caused by the uncertainty of the arrival time of external trucks. This paper proposes an optimization method for the real-time operation scenario which aims to solve the most reasonable operation plan quickly according to the arrivals of external trucks, in which a dynamic upper bound of the optimal solution is derived based on the dynamic programming model of the import containers’ CRP, and an approximate optimal solution can be obtained by minimizing this dynamic upper bound. A heuristic algorithm based on three relocation rules is developed to implement this method, considering the adjustment of the pickup sequence of the target containers. Numerical experiments show that (1) when the number of a batch of target containers is less than 10 (excluding target containers that can be directly picked up), the method proposed in this paper can solve the problem quickly to meet the demand of optimizing real-time pickup operations; (2) compared with other outstanding algorithms, the quality of the solutions obtained by this method is also improved; and (3) this method can be applied to the most container terminals for optimizing real-time pickup operations.

Joint Resource Allocation and Drones Relay Selection for Large-Scale D2D Communication Underlaying Hybrid VLC/RF IoT Systems

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms.

The performance analysis of Adam and SGD in image classification and generation tasks

Optimization problems have a very important leading position in machine learning. A great deal of machine learning algorithms ends up solving optimization problems. Among all the optimization algorithms, gradient methods are the simplest and most commonly used compared to algorithms like Particle Swarm Optimization and Ant Colony Optimization. In the gradient methods, Adaptive Moment Estimation (Adam) and stochastic parallel gradient descent (SGD) are both outstanding algorithms that have helped solve all kinds of deep learning tasks. But which one is better in some certain conditions is still unknown, which means programmers need to try many of the optimizers to have the best choice. Based on some previous researches, this paper study the impact of L2 regularization and weight decay in Adam and SGD with momentum, which turns out in adaptive methods, L2 regularization is not as effective as it is in SGD. It gives the intuition that SGD should outperform Adam in image classification tasks. However, this paper finds things go the other way around by running an experiment using Lenet-5 on MINST. Besides, this paper describes an experiment on Fashion-MINST using DCGAN with both Adam and SGD as optimizers in Generator and Discriminator. The result shows that the generator with SGD produces fake images with higher quality.

Improved differential evolution using two-stage mutation strategy for multimodal multi-objective optimization

Recently, multimodal multi-objective problem (MMOP) has become a popular research field in multi-objective optimization problems. Multimodal multi-objective optimization problem has multiple equivalent Pareto sets corresponding to one same Pareto front, and the goal of solving it is to find all the equivalent Pareto sets, which is divergent from multi-objective optimization problem. To resolve this problem, an improved differential evolution for multimodal multi-objective optimization is proposed. First and foremost, a novel distance indicator, called modified maximum extension distance (MMED), is proposed. Secondly, a two-stage mutation strategy and a novel mutation strategy, namely DE/rand-to-MMEDBest/2, are designed to boost the diversity and convergence in different evolution stages of population. Thirdly, a MMED-based environmental selection that takes individuals in lesser fronts into population is used to enhance the overall performance of the population. Ultimately, the proposed algorithm is compared to multiple selected outstanding algorithms in solving multiple multimodal multi-objective optimization problems. Experimental results indicate that the proposed algorithm is capable of solving multimodal multi-objective optimization problems effectively and has achieved excellent performance.

High-Discrimination Comparison Algorithm for the Comprehensive Evaluation of Innovation Ability in Colleges and Universities under Uncertain Information.

The innovation ability of students is one of the most important objectives that need to be cultivated in colleges and universities. The comprehensive evaluation of innovation ability discussed in the study can be divided into two stages: the first stage can be called preliminary evaluation and its main target is to identify students with innovative potential; in the second stage, the target objects found in the previous stage will be evaluated quantitatively and ranked. However, it is always difficult to quantitatively evaluate the innovation ability by using traditional algorithms. Based on the above analysis, the study proposes an algorithm to quantitatively evaluate the innovation ability with the help of management thought and fuzzy mathematics. Data are the basis of evaluation, and the accuracy of the data directly determines the quality of the algorithm; the data structure of the incompletely probabilistic fuzzy set is proposed in the study; the data structure can fully consider the fuzziness of the problem and the hesitation in the decision-making process; it can save the original detailed data to the maximum extent. Certainly, certain information may be lost or only the value range can be determined; there are usually some unknowns in the evaluation data, and the consistency optimization model is proposed for solving the problem. Usually, there are certain contradictions among the evaluation data; the definition of the consistency degree is proposed in the study; the consistency can be verified in time after all the unknowns are obtained, and the automatic adjustment module will be activated immediately if the value of the consistency degree exceeds the warning threshold. Finally, after verifying the data consistency, the solution can be obtained by solving the optimization model. Several experiments have been carried out to verify the effectiveness and high-discrimination ability of the algorithm proposed in the study; meanwhile, the superiority of the algorithm is further verified through comparisons with other outstanding algorithms.

The optimal emergency decision-making method with incomplete probabilistic information

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences.

Joint spatial and temporal features extraction for multi-classification of motor imagery EEG

The application of brain-computer interface (BCI) has always been limited by low decoding accuracy due to excessive noise in electroencephalogram (EEG) signals. The traditional methods employ some representative features while losing too much information. Deep learning methods have achieved good results, but subject to the insufficient ability of extracting discriminative features from EEG. In this paper, we propose a novel decoding framework that effectively uses spatial and temporal information by time-contained spatial filtering and spatial–temporal analysis network (TSF-STAN) for EEG multi-classification tasks. Firstly, the TSF with the joint one-versus-rest (Joint-OVR) strategy is given to map the signal to a new space, where each category can be more easily distinguished, while preserving the time-domain characteristics. Next, the STAN is designed to extract discriminative spatial and temporal features further with convolutional layers, and then perform classification. Detailed experiments have been carried out to verify the effectiveness of our framework on BCI competition IV-2a and IV-2b datasets of motor imagery (MI) EEG. The results show that our method has outperformed recent outstanding algorithms, with the average accuracy of 83.0% on IV-2a and 88.0% on IV-2b. Spatial and temporal information is well used to obtain better performance, which has a good potential of EEG decoding for application of BCI.

Chaos and intensification enhanced flower pollination algorithm to solve mechanical design and unconstrained function optimization problems

Nature-inspired computation has enjoyed a visible position among the soft computational techniques. Flower Pollination Algorithm (FPA), which is known as one of the outstanding algorithms in this domain, has been shown to be promising in numerous publications. FPA is comprised of two main phases, which are referred to as abiotic and biotic pollination that correspond to local and global search, respectively. It makes use of a user-supplied parameter to switch between them. This parameter is referred to as the switching probability. Thus, it can be put forward that switching probability defines the search characteristic of FPA. The present work introduces several FPA modifications that adopt chaotic maps. Moreover, the developed modifications are further enhanced by using intensifying step sizing procedure that allows a more intensified search towards the end of search. With the help of the introduced chaos in switching probability and incrementally intensifying search, developed FPA modifications are expected to find the hard-to-detect promising regions. Next, such capabilities of chaotic maps are utilized in various building blocks of FPA. Performances of all developed FPA modifications are analysed on the well-known unconstrained real-valued function minimization and mechanical design problems. Finally, appropriate non-parametric statistical analysis is carried out to observe possible statistically significant improvements over the standard FPA. As shown by the experimental study, obtained results induce success of the developed procedures, which clearly add to the capability of the canonical FPA.

Low-Rank Estimation for Image Denoising Using Fractional-Order Gradient-Based Similarity Measure

The aim of this paper is to introduce a novel similarity measure using fractional-order derivative for patch comparison in low-rank image denoising approach. Recently, several outstanding low-rank image denoising algorithms have been proposed. However, these methods have limitations in the sense that certain irrelevant patches can be selected during patch comparison. These undesired patches affect singular values shrinkage and aggregation phases of these approaches. Thus, the fine details and edges of denoised image may not be well preserved. To address this issue, a novel method is proposed in which gradient information is injected in patch comparison using discretized fractional-order derivatives. The advantages of proposed approach are twofold: firstly, the patch comparison becomes more reliable by combining intensity and gradient information; secondly, the fractional-order gradient provides an additional degree of freedom to quantify the gradient information for patch comparison in an efficient way. In addition, the proposed algorithm estimates noise level using geometric details encoded in the image patches. The noise estimation strategy may help in terminating the iterative low-rank approximation. Experimental results on test images reveal that the proposed method performs better than several outstanding algorithms, specifically, in the presence of severe noise levels.

Differential evolution algorithm with dynamic multi-population applied to flexible job shop schedule

This article proposes a novel differential evolution algorithm based on dynamic multi-population (DEDMP) for solving the multi-objective flexible job shop scheduling problem. In DEDMP, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of the subpopulation is dynamically adjusted based on the last search experience. Furthermore, DEDMP is adaptive based on two search strategies, one with strong exploration ability and the other with strong exploitation ability. The selection probability of each search strategy is also dynamically adjusted according to the success rate. Furthermore, the proposed algorithm adopts newly designed mutation and crossover operators and it can directly generate feasible solutions in the search space. To evaluate the performance of DEDMP, DEDMP is compared with some state-of-the-art algorithms on benchmark instances. The experimental results show that DEDMP is better than or at least competitive with other outstanding algorithms.

A hybrid optimizer based on backtracking search and differential evolution for continuous optimization

ABSTRACT This paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.

A Lightweight Multiscale Attention Semantic Segmentation Algorithm for Detecting Laser Welding Defects on Safety Vent of Power Battery

At present, in order to improve the safety performance of power battery, a safety vent is welded on the battery cover to avoid unpredictable explosions. It is vital to detect the laser welding defects on safety vent effectively for product quality. In this paper, a lightweight multiscale attention semantic segmentation algorithm with high accuracy and efficiency was proposed. We built an experimental dataset of safety vent welding defects with a total of 7263 original images, which were collected from a battery manufacturing production line. The main framework of the proposed model consists of four modules: the improved Res2Net serving as the feature extraction sub-module, an attention mechanism, a localization block and a boundary anti-aliasing module. This architecture can segment defects of different sizes and shapes in real-time and get more refined segmentation results simultaneously. To evaluate the method, experiments concerning mean IOU and pixel accuracy were conducted, and an average validation accuracy of 99.4% and the mean IOU of 84.67% were achieved respectively. Furthermore, comparison experiments using some outstanding algorithms on safety vent’s welding defects test dataset were performed. It proves that our method achieved the best performance in terms of model size, computational complexity, efficiency and detection accuracy. Specifically, the model size is only 3.8 MB, and the frames per second (FPS) is 132.3. In brief, the proposed model is suitable for laser welding quality detection on safety vent in an industrial environment. Additionally, our study can provide a reference for designing relevant defect detection tasks using semantic segmentation method.

On Selection of a Benchmark by Determining the Algorithms’ Qualities

The authors got the motivation for writing the article based on an issue, with which developers of the newly developed nature-inspired algorithms are usually confronted today: How to select the test benchmark such that it highlights the quality of the developed algorithm most fairly? In line with this, the CEC Competitions on Real-Parameter Single-Objective Optimization benchmarks that were issued several times in the last decade, serve as a testbed for evaluating the collection of nature-inspired algorithms selected in our study. Indeed, this article addresses two research questions: (1) How the selected benchmark affects the ranking of the particular algorithm, and (2) If it is possible to find the best algorithm capable of outperforming all the others on all the selected benchmarks. Ten outstanding algorithms (also winners of particular competitions) from different periods in the last decade were collected and applied to benchmarks issued during the same time period. A comparative analysis showed that there is a strong correlation between the rankings of the algorithms and the benchmarks used, although some deviations arose in ranking the best algorithms. The possible reasons for these deviations were exposed and commented on.

Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning

This research proposes and tests mechanisms for defining and identifying the critical online consumer reviews that firms could prioritize to optimize their online response strategies, while incorporating the latest artificial intelligence (AI) technology to deal with the overwhelming volume of information. Three sets of analytical tools are introduced: Heuristic Processing, Linguistic Feature Analysis, and Deep Learning-based Natural Language Processing (NLP), to extract review information. Twelve algorithms to classify critical reviews were developed accordingly and empirically tested for their effectiveness. Our econometric analysis of 110,146 online reviews from a chain operation in hospitality industry over seven years identifies six outstanding algorithms. Firm value rating, comment length, valence, and certain consumer emotions, in addition to past comment-response behavior, are found to be superior in predicting incoming review criticality. However, the service attributes such as urgency to reply and the feasibility of actions to take are not as informative.

Credit Risk Assessment Using Learning Algorithms for Feature Selection

Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.

A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges

Distance metric learning is a branch of machine learning that aims to learn distances from the data, which enhances the performance of similarity-based algorithms. This tutorial provides a theoretical background and foundations on this topic and a comprehensive experimental analysis of the most-known algorithms. We start by describing the distance metric learning problem and its main mathematical foundations, divided into three main blocks: convex analysis, matrix analysis and information theory. Then, we will describe a representative set of the most popular distance metric learning methods used in classification. All the algorithms studied in this paper will be evaluated with exhaustive testing in order to analyze their capabilities in standard classification problems, particularly considering dimensionality reduction and kernelization. The results, verified by Bayesian statistical tests, highlight a set of outstanding algorithms. Finally, we will discuss several potential future prospects and challenges in this field. This tutorial will serve as a starting point in the domain of distance metric learning from both a theoretical and practical perspective.

Bipolar N-soft set theory with applications

In this paper, the notion of bipolar N-soft set, which is the bipolar extension of N-soft set, and its fundamental properties are introduced. This new idea is illustrated with real-life examples. Moreover, some useful operations and products on the bipolar N-soft sets are derived. We thoroughly discuss the idempotent, commutative, associative, and distributive laws for these emerging operations and products. Also, we set forth two outstanding algorithms to handle the decision-making problems under bipolar N-soft set environments. We give potential applications and comparison analysis to demonstrate the efficiency and advantages of algorithms.

Cost minimization of scheduling scientific workflow applications on clouds

SummaryWorkflow scheduling with minimum cost is one of the most challenging problems for the users who need to execute a large‐scale scientific application on a cloud platform. However, traditional methods are hard to cover the highly complex applications and ignore the billing model of the public clouds. In this paper, we address the problem of scheduling a scientific application on cloud platform from the perspective of users. First, we propose a Satisfiability Modulo Theories (SMT) based algorithm to schedule a scientific application on cloud platform, the SMT algorithm constructs the scheduling problem to first‐order logic expressions and checks the expressions by solvers, which minimizes the number of Virtual Machine instances (VMs) allocated to the application. Furthermore, due to the hourly payment of cloud, we develop a heuristic algorithm called Multiple Strategies Algorithm (MSA) which determines the minimum instance hours of a scientific application deployed on VMs. At last, we combine the proposed SMT based algorithm and the MSA to a framework named SMT‐MSA, and compare it with other outstanding algorithms in experiments, the results show that, in most of cases, our algorithms reduce more cost than the other three methods which are HEFT, MSMD and IC‐PCPD2.

Transferring Common Spatial Filters With Semi-Supervised Learning for Zero-Training Motor Imagery Brain-Computer Interface

Common spatial pattern (CSP) has been proved to be one of the most efficient feature-extracting methods for brain-computer interfaces (BCIs), especially for motor imagery BCI. However, CSP is a supervised method and performs poorly when there are not enough labeled data. This paper aims to construct a minimum-training BCI, which means there are only a few labeled data, even none labeled data for target subjects. Under this condition, conventional CSP cannot work well. Therefore, source data (related labeled data from other subjects) are exploited and common filters across subjects are obtained using a clustering method. After that, features are extracted and a semi-supervised support vector machine which transfers knowledge across subjects is proposed. The experiments illustrate the effectiveness of our algorithm. When there are none labeled data for target subjects, our algorithm outperforms two state-of-the-art algorithms in semi-supervised learning field, and as the amount of unlabeled data for target subjects becomes larger, the performance of our algorithm grows better and better, which is suitable for online use. When the amount of labeled data for target subjects (denoted as M in this paper) is small, our algorithm also shows its strength compared with corresponding outstanding algorithms. For dataset IVa of BCI competition III, our algorithm performs the best for all the subjects excluding “aw”when M = 20. Compared with counterparts, our method outperforms 6.6% for “aa,”19.3% for “al,”11.8% for “av,”and 9.7% for “ay”on average, respectively. When M = 40, our method performs the best for “al”and “av”. It averagely outperforms 8.0% for “al”and 15.3% for “av,”respectively. For GigaDataset, averagely our method outperforms 4.4% for “sbj2,”5.0% for “sbj4,”and 7.1% for “sbj5”when M = 20, respectively. When M = 40, our algorithm performs the best only for “sbj5.”It averagely outperforms 8.6% compared with counterparts. Although the performances of our method are not the best for all the conditions, its performances are very robust and competitive.