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Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems

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APPLIED INTELLIGENCE 2023年第6期53卷 6763-6799页

作者： He, Jieguang Peng, Zhiping Cui, Delong Qiu, Jingbo Li, Qirui Zhang, Hao Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault Maoming Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

The Sooty Tern Optimization algorithm (STOA) is a newly proposed bio-inspired algorithm that mimics the migration and attacking behaviors of the sea bird sooty tern in nature. STOA has several excellent advantages, including fewer parameters, a simple structure, a fast convergence rate, and high exploitation. Nevertheless, it is difficult to find the global optimal solution and prone to losing population diversity when dealing with complex optimization problems due to its single search guidance strategy and position update method. An enhanced STOA (ESTOA) is proposed to address these shortcomings that incorporates multiple search guidance strategies and position update modes. In terms of search guidance, in addition to the best individual in the original STOA, the mean individual and a randomly selected individual are also designed to guide the search. Six position update modes are proposed in conjunction with the guidance strategies, including one improved scaling mode with an extended spiral radius and five other modes based on offset operations. Due to their distinct design objectives, these guidance strategies and position update modes exhibit varying levels of search intensity and optimization effect. However, they complement one another and work cooperatively to achieve a good balance of global exploration and local exploitation. Several widely used sets of benchmark functions with a wide range of dimensions and varying degrees of complexity are used to validate ESTOA's performance. The obtained results are compared to those of other state-of-the-art optimization algorithms in terms of convergence accuracy and a variety of numerical performance evaluation parameters. A significant improvement in solution quality demonstrates that ESTOA can increase population diversity and maintain a good balance between global exploring and local exploiting abilities.

关键词： Sooty Tern Optimization algorithm bio-inspired algorithm Optimization Search guidance Position update

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An improved artificial algae algorithm integrated with differential evolution for job-shop scheduling problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第4期34卷 1763-1778页

作者： Ibrahim, Abdelmonem M. Tawhid, Mohamed A. Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada

For the past decades, practitioners and researchers have been fascinated by the job-shop scheduling problems (JSSP) and have proposed many pristine meta-heuristic algorithms to solve them. JSSP is an NP-hard problem and a combinatorial optimization problem. This paper proposes a highly efficient and superior performance strategy for the artificial algae algorithm (AAA) integrated with the differential evolution (DE), denoted AAADE, to solve JSSP. The new movement algae colonies using DE operators are introduced to the proposed hybrid artificial algae algorithm and DE (AAADE). To improve AAA's intensification ability, the movement using the DE mutation is implemented into the AAA. In the new hybrid method, the DE crossover can update its position based on both movements (helical and DE movements) to increase randomization. Two categories of problems verify the efficiency and validity of the proposed hybrid algorithm, AAADE, namely, CEC 2014 benchmark functions and different job-shop scheduling problems. The AAADE results are compared with other algorithms in the literature. Hence, comparisons numerical experiments validated and verified the quality of the proposed algorithm. Experimental results validate the effectiveness of the proposed hybrid method in producing excellent solutions that are promising and competitive to the state-of-the-art heuristic-based algorithms reported in the literature in most of the benchmark functions in CEC'14 and JSSP.

关键词： Artificial algae algorithm bio-inspired algorithm Differential evolution Job-shop scheduling problem Metaheuristics

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Modeling and optimization of direct urea-hydrogen peroxide fuel cell using the integration of artificial neural network and bio-inspired algorithms

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JOURNAL OF ELECTROANALYTICAL CHEMISTRY 2022年第0期922卷 1页

作者： Thi Hoa Le Thakur, Deepika Phan Khanh Thinh Nguyen Gachon Univ Dept Chem & Biol Engn 1342 Seongnamdaero Seongnam Si 13120 Gyeonggi Do South Korea Seoul Natl Univ Dept Agr Forestry & Bioresources 1 Gwanak Ro Seoul 08826 South Korea

In this study, the performance of direct urea-hydrogen peroxide fuel cells (DUHPFCs) was modeled and optimized for the first time by artificial intelligence techniques. Accordingly, an artificial neural network (ANN) model was developed to describe the DUHPFC's voltage relying on basic designing and operating parameters (i.e., anode catalyst properties, urea concentration, KOH concentration, temperature, and feeding flow rate). A two-hidden layer-ANN with 7-10-6-1 topology using the Levenberg-Marquardt algorithm, logistic sigmoid function, and a training data proportion of 80 % was the best suitable model. A mean squared error (MSE) and R-value were estimated to be 0.51 x 10(-4) and 0.9993, respectively, indicating a good prediction capability. Subsequently, the bio-inspired algorithms (BIAs), including particle swarm optimization (PSO) and genetic algorithm (GA), were employed to identify the optimal process parameters. Similar optimum results were obtained by both algorithms, although ANN-PSO performed faster than ANN-GA. When Ni0.2Co0.8/Ni-foam was used as the anode catalyst, the calculated maximum power density was 45.6 mW/cm(2) under urea concentration of 1.4 M, KOH concentration of 6.2 M, temperature of 70 degrees C, and flow rate of 5.9 mL/min. However, the electrical energy recovery was only 2.6 % under such optimal conditions, suggesting that other factors, especially novel urea-electrooxidation catalysts, should be investigated further.

关键词： Direct urea-hydrogen peroxide fuel cell Artificial neural network Modeling Optimization bio-inspired algorithm

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Review and empirical analysis of sparrow search algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 10867-10919页

作者： Yue, Yinggao Cao, Li Lu, Dongwan Hu, Zhongyi Xu, Minghai Wang, Shuxin Li, Bo Ding, Haihua Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Intelligent Informat Syst Inst Wenzhou 325035 Peoples R China Wenzhou Univ Technol Wenzhou Key Lab Intelligent Lifeline Protect & Eme Wenzhou 325035 Peoples R China

In recent years, swarm intelligence algorithms have received extensive attention and research. Swarm intelligence algorithms are a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithms provide new ideas and new ways to solve some practical problems, and they have made positive progress in fields such as combinatorial optimization, task scheduling, process control, engineering prediction, and image processing. In particular, the sparrow search algorithm is a new type of group intelligence optimization algorithm inspired by the group foraging behavior to perform local and global search by imitating the foraging and anti-predation behavior of sparrows. In view of the shortcomings of the original sparrow search algorithm, such as its easy fall into local optimum, slow convergence speed, and low convergence accuracy, scholars at home and abroad have improved the sparrow search algorithm and have made practical applications in various fields. Firstly, this paper introduces the basic principle of sparrow search algorithm, analyzes the factors affecting the performance of the algorithm, further proposes the improvement strategy of the algorithm, and performs function test comparison and performance analysis with particle swarm optimization algorithm, monarch butterfly algorithm, colony spider algorithm, and pigeon swarm optimization algorithm. After that, the application and development of the sparrow search algorithm in power grid load forecasting, image processing, path tracking, wireless sensor network routing performance optimization, wireless location, and fault diagnosis are described. Finally, combined with the performance characteristics and application direction of the sparrow search algorithm, the future research and development direction of the sparrow search algorithm is prospected.

关键词： Swarm intelligence Evolutionary computation bio-inspired algorithm Sparrow search algorithm Improvement strategy

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Recent trends in intelligent transportation systems using big data analysis

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2023年第1期72卷 29-49页

作者： Qurashi, Mahvash Iftikhar Ali, Zain Anwar Shafiq, Muhammad Lodhi, Arsalan Khan Sir Syed Univ Engn & Technol Comp Engn Dept Karachi Sindh Pakistan JiaYing Univ Sch Phys & Elect Engn Meizhou Peoples R China Sir Syed Univ Engn & Technol Elect Engn Dept Karachi Sindh Pakistan

Internet of Things (IoT) is the core concept for smart cities. The foremost functionality of IoT is to access the data collected from the shared devices over the communication technologies from the sensors. The data from the various sources are mostly heterogeneous. Big data organises the data and brings efficiency by increasing performance. Big data works on 3Vs (Volume, Velocity, Variety). The massive generation of data refers to Volume, the speediness of data generation refers to Velocity and the generation of data in either structured or unstructured format is Variety. The Intelligent Transportation System is an advanced technology that provides state-of-the-art facilities for different traffic management modes and keeps the users well informed about the safe journey. In this research the recent applications of ITS with different trends like data pre-processing, artificial intelligent algorithms, computer vision, bio-inspired algorithms and future works are discussed.

关键词： smart city intelligent transportation system AI algorithm bio-inspired algorithm computer vision big data analytics

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Dual-filtering (DF) schemes for learning systems to prevent adversarial attacks

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COMPLEX & INTELLIGENT SYSTEMS 2023年第4期9卷 3717-3738页

作者： Dasgupta, Dipankar Gupta, Kishor Datta Univ Memphis Memphis TN 38152 USA

Defenses against adversarial attacks are essential to ensure the reliability of machine-learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. We proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through anomaly (outlier) detectors before making the final decision. Experimental results (using benchmark data-sets) demonstrated that our dual-filtering strategy could mitigate adaptive or advanced adversarial manipulations for wide-range of ML attacks with higher accuracy. Moreover, the output decision boundary inspection with a classification technique automatically affirms the reliability and increases the trustworthiness of any ML-based decision support system. Unlike other defense techniques, our dual-filtering strategy does not require adversarial sample generation and updating the decision boundary for detection, makes the ML defense robust to adaptive attacks.

关键词： Adversarial machine learning Computer security bio-inspired algorithm Negative selection algorithm

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Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

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bioMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartB期72卷 103340-103340页

作者： Isaac, Anisha Nehemiah, H. Khanna Dunston, Snofy D. Christo, V. R. Elgin Kannan, A. Anna Univ Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India Anna Univ Comp Sci & Engn Chennai 600025 Tamil Nadu India Anna Univ Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India

A Computer-Aided Diagnosis (CAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been developed. The lung tissues are segmented from the chest CT slices using Spatial Fuzzy C-Means (SFCM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and runlength are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and CT emphysema database (CTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the CAD system without performing FS.

关键词： Competitive coevolution Segmentation Spatial fuzzy C-means clustering bio-inspired algorithm Spider monkey optimization Paddy field Feature selection

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High-Precision Real-Time Flow Prediction in a Multi-tributary River System: A bio-inspired Dynamic Neural Network Model

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EARTH SYSTEMS AND ENVIRONMENT 2025年 1-17页

作者： Yang, Jinying Liu, Bao Xu, Mei Marcos-Martinez, Raymundo Gao, Lei China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China Commonwealth Sci & Ind Res Org CSIRO Canberra ACT 2601 Australia Commonwealth Sci & Ind Res Org CSIRO Waite Campus Glen Osmond SA 5064 Australia

Floods are among the most severe natural disasters globally, particularly in densely populated areas with extensive agriculture, concentrated rivers, and abundant rainfall. In recent years, human activities have altered river confluence conditions, exacerbating the frequency and severity of floods. To address the limitations of existing multi-tributary stream flow prediction models, which suffer from poor real-time performance and low prediction accuracy, we developed a bio-inspired neural network (bio-NN) model motivated by a cooperative regulation mechanism in biological systems. Considering the problem that there is less feedback information in existing neural networks, the proposed model combines a biohormone multi-level nonlinear feedback regulation mechanism with a neural network. This enhances traditional neural networks by improving network structure and dynamically incorporating feedback information, allowing real-time optimization and improving optimization speed and precision over time. We tested the bio-NN model by applying it to predict river flow along the lower Murray River in Australia. To obtain deeper insights into the performance of bio-NN, indicators such as NSE, RSR, PCC, and KGE, were determined in the basin. The simulation demonstrated its superior performance, achieving a Nash-Sutcliffe efficiency coefficient (NSE) of 0.991, root mean squared to standard deviation ratio (RSR) of 0.096, a Pearson's correlation coefficient (PCC) of 0.996, and a Kling-Gupta efficiency coefficient (KGE) of 0.995. Compared to a back propagation neural network (BP-NN), a dynamic learning BP-NN, and a self-feedback BP-NN, the bio-NN showed significant improvements in prediction performance: improved by 8-65% (NSE), 4-28% (PCC), 67-85% (RSR), 9-27% (KGE). The results underscore bio-NN's capability to significantly enhance the accuracy and stability of flood prediction models.

关键词： bio-inspired algorithm Flow Prediction Neural Network River System Real-time Prediction

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Comparison Between Single and Multi-objective Clustering algorithms: MathE Case Study 1

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4th International Conference on Optimization Learning algorithms and Applications

作者： Azevedo, Beatriz Flamia Rocha, Ana Maria A. C. Fernandes, Florbela P. Pacheco, Maria F. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Aveiro Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M P-5300253 Braganca Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

This paper compares the results obtained for four single clustering algorithms with a multi-objective clustering approach. For this, a dataset describing the student's behavior within the Linear Algebra topic on the MathE e-learning platform is used. This dataset aids in understanding student performance and engagement in MathE to support the development of an intelligent system to tailor the platform's resources to users's needs. The four algorithms suggested two clusters as the optimal solution for the dataset. However, this binary categorization did not provide meaningful insights into the proposal of the MathE platform;that is, it did not provide a customized system according to individual needs. Thus, this study uses the multi-objective clustering algorithm, which results in a set of non-dominated solutions, providing decision-makers with a broader range of options to choose the solution that best meets their needs. The results demonstrate the main benefits of the proposed human-in-the-loop multi-objective approach since it provides several optimal solutions and allows the decision-maker to apply fundamental knowledge to define the most appropriate solution to the problem based on previous knowledge.

关键词： multi-objective clustering automatic clustering optimization bio-inspired algorithm

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Bees, Bats and Glowworms: Swarm algorithms for Optimizing Industrial Plants from the Bottom-Up 15th

Bees, Bats and Glowworms: Swarm Algorithms for Optimizing In...

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15th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Umlauft, M. Gojkovic, M. Harshina, K. Majbour, K. Schranz, M. Lakeside Labs GmbH Klagenfurt Austria

ISBN: (纸本)9783031553257;9783031553264

The scheduling problem in production plants with diverse product ranges is NP-hard, posing challenges in finding optimal solutions. To overcome this, we propose a novel approach utilizing three bio-inspired optimization algorithms: artificial bee colony (ABC), bat, and glowworm algorithm. We apply these algorithms to address the job-shop scheduling problem and overcome computational barriers associated with traditional methods. Unlike previous studies, our approach departs from using these algorithms for global optimization within the solution space. Instead, we adopt a bottom-up strategy, directly applying them as verbatim swarm intelligence algorithms. Focusing on a semiconductor production plant, we employ agent-based modeling in the NetLogo simulation platform. By mapping bees, bats, and glowworms to plant entities like lots and machines, we establish direct correspondences. Agents interact with each other and the environment based on local rules, resulting in the emergence of desired global behavior-the industrial plant optimization. To evaluate performance, we compare our approach to a baseline algorithm employing engineered heuristics like First-In-First-Out (FIFO) and filling fullest batches first. Through this comparison, we assess the effectiveness of the bottom-up algorithms. Our results show promising performance improvements achieved with these algorithms, which rely on low-effort local calculations.

关键词： Swarm intelligence bio-inspired algorithm Artificial bee algorithm Bat algorithm Glowworm algorithm Flexible job-shop scheduling Agent-based modeling

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