Applied Soft Computing最新文献

{"title":"A robust rank aggregation method for malicious disturbance based on objective credit","authors":"Dongmei Chen ,&nbsp;Yu Xiao ,&nbsp;Huan Zhu ,&nbsp;Ye Deng ,&nbsp;Jun Wu","doi":"10.1016/j.asoc.2024.112471","DOIUrl":"10.1016/j.asoc.2024.112471","url":null,"abstract":"<div><div>Rank aggregation is a task of combining individual rankings into a consensus, which has widespread applications in many areas, ranging from social choice to information retrieval. As some users may have incentives to disrupt the aggregated ranking for enormous benefits, making rank aggregation methods robust to malicious disturbance becomes a crucial challenge. In this study, we propose a robust rank aggregation method based on objective credit. The underlying idea is that a consensus ranking is obtained by combining multiple input rankings with users’ credit, while users’ credit is reflected by the differences between their input rankings and the consensus ranking. This idea motivates a novel iterative algorithm, which iteratively updates a consensus ranking weighted by users’ credit and modifies users’ credit by measuring the differences from a consensus ranking until all credit converges. In this way, the algorithm objectively assigns different credit to users, leading to a more reliable aggregated ranking. Extensive experiments on synthetic and real data demonstrate the superior performance of our method over state-of-the-art baselines.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112471"},"PeriodicalIF":7.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of advanced persistent threat: A genetic programming approach","authors":"Abdullah Al Mamun ,&nbsp;Harith Al-Sahaf ,&nbsp;Ian Welch ,&nbsp;Masood Mansoori ,&nbsp;Seyit Camtepe","doi":"10.1016/j.asoc.2024.112447","DOIUrl":"10.1016/j.asoc.2024.112447","url":null,"abstract":"<div><div>Advanced Persistent Threats (APTs) are an intimidating class of cyberattacks known for their persistence, sophistication, and targeted nature. These attacks, coordinated by highly motivated adversaries, pose a grave risk to organizations and individuals, often operating stealthily and evading detection. While existing research primarily focuses on applying Machine Learning (ML) methods to analyze network traffic data for APT detection, this article introduces a novel approach that utilizes Genetic Programming (GP). The proposed method not only detects APT attacks but also identifies their specific life cycle stages through the evolutionary capabilities of GP. Its effectiveness lies in its ability to excel in detecting intricate patterns, even within classes with a limited number of instances, a feat that is often challenging for traditional ML techniques. The method involves evolving and optimizing its models to effectively learn and adapt to complex APT behaviors. Experimentation with a publicly available dataset showcases the efficacy of the proposed method across diverse APT stages. The results demonstrate that the proposed method, GPC, achieves a 3.71% improvement in balanced accuracy compared to the best-performing model from related works. Moreover, a thorough analysis of the best-evolved GP model uncovers valuable insights about identified features and significant patterns. This research advances the APT detection paradigm by leveraging GP’s capabilities, providing a fresh and effective perspective on countering these persistent threats.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112447"},"PeriodicalIF":7.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fuzzy logic constrained particle swarm optimization algorithm for industrial design problems","authors":"Bo Sun ,&nbsp;Peixi Peng ,&nbsp;Guang Tan ,&nbsp;Mingjun Pan ,&nbsp;Luntong Li ,&nbsp;Yonghong Tian","doi":"10.1016/j.asoc.2024.112456","DOIUrl":"10.1016/j.asoc.2024.112456","url":null,"abstract":"<div><div>Most of the industrial design problems have non-linear constraints, high computational cost, non-convex, complicated, and large number of solution spaces. This poses a challenge for algorithms to effectively handle constraints and improve solution accuracy. To address these challenges, a fuzzy logic particle swarm optimization algorithm incorporating a correlation-based constraint handling method (FILPSO-SCA<span><math><mi>ɛ</mi></math></span>) is proposed. In FILPSO-SCA<span><math><mi>ɛ</mi></math></span>, an adaptive <span><math><mi>ɛ</mi></math></span> constraint handling method with correlation analysis is introduced to dynamically adjust the utilization of constraints and the objective function information. The particle swarm optimization algorithm is employed as the searcher, and to augment its search capability, a set of fuzzy logic rules integrating individual feasibility is designed. These rules dynamically generate parameters in learning strategies by considering fitness and the distance between individuals. To mitigate premature convergence problems, we introduce an individual learning mechanism utilizing stagnation detection. 28 constrained optimization problems and 2 industrial design problems are utilized for comparison with 16 well-known constrained evolutionary algorithms. The proposed algorithm ranks first among the 16 comparative algorithms, with a success rate of 100% in solving industrial design problems.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112456"},"PeriodicalIF":7.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-shard transaction optimization based on community detection in sharding blockchain systems","authors":"Peng Han ,&nbsp;Linzhao Sun ,&nbsp;Quang-Vi Ngo ,&nbsp;Yuanyuan Li ,&nbsp;Guanqiu Qi ,&nbsp;Yiyao An ,&nbsp;Zhiqin Zhu","doi":"10.1016/j.asoc.2024.112451","DOIUrl":"10.1016/j.asoc.2024.112451","url":null,"abstract":"<div><div>Blockchain systems have always faced the challenge of performance bottlenecks, and sharding technology is considered a promising mainstream on-chain scalability solution to solve this problem. Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions, it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit. Therefore, this paper aims to reduce the proportion of cross-shard transactions by dividing accounts with frequent transactions into the same shard, thereby improving system throughput. This paper builds a hypergraph based on historical transaction data to represent the diverse transaction relationships between accounts, and formulates the account division problem in the blockchain as a community discovery problem on the hypergraph structure. A time-aware community detection algorithm is proposed to partition accounts by considering the sustainability of transaction relationships between accounts. This also solves the problem of community detection algorithms tending to partition into larger shards. In addition, this paper builds a local Ethereum test network and implements the proposed algorithm on a real transaction dataset. Experimental results show that this algorithm can reduce the proportion of cross-shard transactions from about 95% to about 10%. Furthermore, it shows superior performance in terms of transaction throughput and latency compared with other community detection-based account partitioning algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112451"},"PeriodicalIF":7.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surrogate-assisted fully-informed particle swarm optimization for high-dimensional expensive optimization","authors":"Chongle Ren ,&nbsp;Qiutong Xu ,&nbsp;Zhenyu Meng ,&nbsp;Jeng-Shyang Pan","doi":"10.1016/j.asoc.2024.112464","DOIUrl":"10.1016/j.asoc.2024.112464","url":null,"abstract":"<div><div>Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be powerful optimization tools for tackling Expensive Optimization Problems (EOPs) where a limited number of function evaluations are available. However, many SAEAs are only designed for low- or medium-dimensional EOPs. Existing SAEAs are challenging to address High-dimensional EOPs (HEOPs) owing to the curse of dimensionality and lack of powerful exploitation capacity. To tackle HEOPs efficiently, a Surrogate-Assisted Fully-informed Particle Swarm Optimization (SA-FPSO) algorithm is proposed in this paper. Firstly, a generation-based Social Learning-based PSO (SLPSO) is adopted to explore the whole decision space with the help of the global surrogate model. Secondly, the fully-informed search scheme is incorporated into the framework of SLPSO to improve its exploitation capacity in the surrogate-assisted search environment. Thirdly, a local space identification strategy is proposed to determine the search range for the local surrogate-assisted search. Seven commonly used expensive benchmark functions with dimensions ranging from 30D to 300D are used to verify the performance of SA-FPSO for HEOPs. Experiment results indicate that SA-FPSO obtains superior performance over several state-of-the-art SAEAs both in terms of convergence speed and solution accuracy.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112464"},"PeriodicalIF":7.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A p-ary Choquet-based multicriteria decision-making model for customer-oriented product design scheme selection","authors":"Yu Gao,&nbsp;Mei Cai ,&nbsp;Jingmei Xiao,&nbsp;Guang Yang","doi":"10.1016/j.asoc.2024.112459","DOIUrl":"10.1016/j.asoc.2024.112459","url":null,"abstract":"<div><div>As an efficient customer-oriented product design tool for converting customer requirements (CRs) into quality characteristics (QCs) of a product, quality function deployment (QFD) is applied to the selection of new product design schemes. However, the effective implementation of QFD is hampered by two fundamental challenges: (a) biases in people’s understanding of customer satisfaction and (b) the ambiguously assessed relationship between CRs and QCs because of the inherent uncertainty in human judgment. Therefore, this paper provides a <em>p</em>-ary Choquet-based multicriteria decision-making model to rank new product design schemes considering customer psychological and risk attitudes. The selection of a new product design scheme is divided into two stages. In the first stage, the <span><math><mi>p</mi></math></span>-ary Choquet integral converts the objective technical parameters of a new product design scheme into utility, which is the subjective feeling from the perspective of two-factor theory. Then, the integration of 2-additive measures and the Choquet integral is used to account for the redundancy and complementary effects among customer requirements. Finally, a case study of an electric vehicle manufacturing company and a comparison analysis are presented to illustrate the validity of our proposal. The <em>p</em>-ary Choquet-based multicriteria decision-making model realizes customer-oriented product design scheme selection by systematically analyzing customer preferences and bridging the gap between products that are emotionally recognized by customers and those that exhibit excellent performance in terms of functionality and cost.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112459"},"PeriodicalIF":7.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ensemble learning algorithm for optimization of spark ignition engine performance fuelled with methane/hydrogen blends","authors":"Mohammad-H. Tayarani-N. ,&nbsp;Amin Paykani","doi":"10.1016/j.asoc.2024.112468","DOIUrl":"10.1016/j.asoc.2024.112468","url":null,"abstract":"<div><div>The increasing global demand for sustainable and cleaner transportation has led to extensive research on alternative fuels for Internal Combustion (IC) engines. One promising option is the utilization of methane/hydrogen blends in Spark-Ignition (SI) engines due to their potential to reduce Green House Gas (GHG) emissions and improve engine performance. However, the optimal operation of such an engine is challenging due to the interdependence of multiple conflicting objectives, including Brake Mean Effective Pressure (BMEP), Brake Specific Fuel Consumption (BSFC), and nitrogen oxide (NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span>) emissions. This paper proposes an evolutionary optimization algorithm that employs a surrogate model as a fitness function to optimize methane/hydrogen SI engine performance and emissions. To create the surrogate model, we propose a novel ensemble learning algorithm that consists of several base learners. This paper employs ten different learning algorithms diversified via the Wagging method to create a pool of base-learner algorithms. This paper proposes a combinatorial evolutionary pruning algorithm to select an optimal subset of learning algorithms from a pool of base learners for the final ensemble algorithm. Once the base learners are designed, they are incorporated into an ensemble, where their outputs are aggregated using a weighted voting scheme. The weights of these base learners are optimized through a gradient descent algorithm. However, when optimizing a problem using surrogate models, the fitness function is subject to approximation uncertainty. To address this issue, this paper introduces an uncertainty reduction algorithm that performs averaging within a sphere around each solution. Experiments are performed to compare the proposed ensemble learning algorithm to the classical learning algorithms and state-of-the-art ensemble algorithms. Also, the proposed smoothing algorithm is compared with the state-of-the-art evolutionary algorithms. Experimental studies suggest that the proposed algorithms outperform the existing algorithms.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"168 ","pages":"Article 112468"},"PeriodicalIF":7.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-UAV interactive perception: Skeleton-based iterative perspective optimization algorithm for UAV patrol tracking of large-scale pedestrian abnormal behavior","authors":"Ziao Wang ,&nbsp;Tao Chen ,&nbsp;Jian Chen","doi":"10.1016/j.asoc.2024.112467","DOIUrl":"10.1016/j.asoc.2024.112467","url":null,"abstract":"<div><div>This paper presents a system framework for UAV patrolling pedestrian abnormal behavior in public places based on human key point features and a skeleton based UAV large-scale pedestrian patrol viewpoint optimal with PID-iterative learning control algorithm is designed. The system framework provides early warning of abnormal pedestrian behavior through images obtained by UAV and human key point recognition by OpenPose. Aiming at the problem of recognizability of images acquired during UAV patrol, an algorithm inspired by iterative learning control for tracking large crowds with human torso and shoulder skeleton information is proposed and a PID-iterative learning control algorithm is designed to improve the control effect under the situation that pedestrian motion has an approximate repeatability. The algorithm is able to enhance the number of photographed pedestrians and increase the sustained observation time of their behaviors. Finally, the usability and effectiveness of the algorithm are verified by experiment results. The proposed method will contribute to the research of interactive perception technology in embodied intelligence.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112467"},"PeriodicalIF":7.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hesitant fuzzy linguistic preference consistency-driven consensus model with large-scale group interaction measure for venture capital investment selection","authors":"Yuanyuan Liang ,&nbsp;Yanbing Ju ,&nbsp;Xiao-Jun Zeng ,&nbsp;Peiwu Dong ,&nbsp;Mihalis Giannakis ,&nbsp;Hengxia Gao ,&nbsp;Tianyu Zhang","doi":"10.1016/j.asoc.2024.112453","DOIUrl":"10.1016/j.asoc.2024.112453","url":null,"abstract":"<div><div>Recently, consensus-based large-scale group decision making (LSGDM) has been widely interactive with the study of social network, clustering and trust-based concepts. This study develops a novel hesitant fuzzy linguistic preference consistency-driven consensus model with interaction measure for large-scale group decision makers (DMs) in social networks. Firstly, directed social network is constructed by measuring the similarity between incomplete hesitant fuzzy linguistic preference relation (HFLPR) matrices. Community detection method is further conducted to categorize DMs into several communities. Secondly, driven by exploring the consistency of HFLPR matrices and interactive trusts between DMs, a novel optimization model is established to estimate the missing elements. Thirdly, the 2-order additive fuzzy measures of different coalitions between divided communities for capturing their fully or partially interactions are derived by a consistency-based optimization model. Accordingly, the attitudinal Choquet integral operator is employed to aggregate preferences into the collective one. Fourthly, a consensus improving mechanism is devised to achieve the unanimous agreement of DMs characterized by the bounded confidence. Personalized and specific adjustment scales obtained by investigating interval consistency of HFLPRs are provided in support of DMs’ modifications. Finally, an illustrative case on syndicated venture capital investment selection is conducted and related simulation analyses are performed to elucidate the feasibility and validity of the proposed methods. The comparisons with other approaches reveal the superiority and improvement of our proposal.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112453"},"PeriodicalIF":7.2,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable rough neural network for lung nodule diagnosis","authors":"Huanan Bao ,&nbsp;Guoyin Wang ,&nbsp;Chen Liu ,&nbsp;Qun Liu ,&nbsp;Qiuyu Mei ,&nbsp;Changhua Xu ,&nbsp;Xin Wang","doi":"10.1016/j.asoc.2024.112450","DOIUrl":"10.1016/j.asoc.2024.112450","url":null,"abstract":"<div><div>Computer-aided diagnosis (CAD) systems based on deep learning have shown significant potential in lung nodule diagnosis, providing substantial assistance to medical professionals. However, the inherent lack of interpretability in deep learning models and the uncertainty of annotations limit their widespread application. We propose that uncertain annotations actually imply additional valuable information that can enhance both model performance and interpretability. To address these challenges, we have developed a novel soft computing methodology integrating rough sets with deep neural networks. Firstly, this methodology employs rough sets to process uncertain region of interest (ROI) annotations into upper and lower approximations. Secondly, a novel rough neuron is designed to predict these approximations. Thirdly, the newly proposed region-constraint strategy embeds interpretable radiological domain knowledge into the neural network. Finally, this methodology proposes interpretation curves and regional consistency metrics to quantitatively evaluate the model’s interpretability. We conducted extensive comparison experiments on LIDC-IDRI and LNDb public benchmarks. Detailed experimental results demonstrate that by maximally retaining uncertain samples, the proposed method achieves classification accuracies of 84.6% and 89.74%, and mean absolute errors of 0.4988 and 0.5208 in attribute prediction, representing improvements of 3.4% and 2.5%, respectively, over the backbone networks.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112450"},"PeriodicalIF":7.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}