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Multi-Strategy Improved Artificial Rabbit algorithm for QoS-Aware Service Composition in Cloud Manufacturing

algorithmS 2025年第2期18卷 107-107页

作者： Deng, Le Shu, Ting Xia, Jinsong Zhejiang Sci Tech Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China

Cloud manufacturing represents a pioneering service paradigm that provides flexible, personalized manufacturing services to customers via the Internet. Service composition plays a crucial role in cloud manufacturing, which focuses on integrating dispersed manufacturing services in the cloud platform into a complete composite service to form an efficient and collaborative manufacturing solution that fulfills the customer's requirements, having the highest service quality. This research presents the multi-strategy improved artificial rabbit optimization (MIARO) technique, designed to overcome the limitations with the original method, which often risks converging to local optima and have poor solution quality when dealing with optimization problems. MIARO helps the algorithm escape local optimality with L & eacute;vy flights, extends local search with the golden sine mechanism, and expands variability with Archimedean spiral mutations. MIARO is experimented on 23 benchmark functions, 3 engineering design problems, and QoS-aware cloud service composition (QoS-CSC) issues at various sizes, and the experimental findings indicate that MIARO delivers outstanding performance and offers a viable solution to the QoS-CSC problem.

关键词： artificial rabbit optimization meta-heuristic algorithm cloud manufacturing engineering design combination optimization

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A Novel Hybrid Method Using Grey Wolf algorithm and Genetic algorithm for IoT Botnet DDoS Attacks Detection

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-61页

作者： Maazalahi, Mahdieh Hosseini, Soodeh Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran

The Internet of Things (IoT) is a vast network of interconnected physical objects that has improved the conditions for a computer-based physical world and improved efficiency. With the increase in communication in an IoT system, Internet security has decreased, and the most dangerous and sophisticated attacks in the IoT have emerged, i.e., DDoS and Botnet attacks. DDoS attacks are a serious threat to the availability of Internet services, especially since botnets can now be launched by almost anyone. In this situation, the use of an intrusion detection system (IDS) is essential to detect intruders and maintain the security of IoT networks. In this paper, a new IDS is proposed to detect IoT-Botnet DDoS attacks. This IDS is a new three-phase system, the first phase is related to preprocessing on the dataset and the second phase includes a new hybrid method for feature selection using filter and wrapper methods based on the Grey Wolf (GW) algorithm and genetics called GW-GA. In this method, the initial population is randomly selected and then at each stage, feature selection is done by both algorithms simultaneously and the final answer is compared and the best solutions are given as a new population to both algorithms and the third phase includes the use of machine learning and metaheuristic algorithms as classifiers. In the proposed method and to verify the performance, it is evaluated using the large BOT-IoT dataset. The results show that the proposed method significantly reduces the feature and also increases the classification accuracy compared to other methods, and the RF and Bagging algorithms have achieved a maximum recognition accuracy of 0.999. The dimensions of BOT-IoT have been reduced from 46 features to 12.

关键词： Evolutionary algorithms Genetics algorithm Grey Wolf algorithm Botnet attack DDOS meta-heuristic algorithm Machine learning algorithms

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Improving performance of node clustering in wireless sensor networks using meta-heuristic algorithms and a novel validity index

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JOURNAL OF SUPERCOMPUTING 2019年第11期75卷 7550-7572页

作者： Sohrabi, Mohammad Karim Alimirzaee, Somayyeh Islamic Azad Univ Dept Elect & Comp Engn Semnan Branch Semnan Iran

The use of wireless sensor networks has significantly increased in the last decade. These networks consist of a large number of small sensors, which are efficient tools for data collection from different environments. The data collected by these sensors will be usually transmitted to a base station that will provide the end user's data. In order to improve scalability of such networks, sensor nodes can be grouped into non-overlapped clusters. These clusters create a hierarchical design in wireless sensor networks that leads to better energy utilization and thus increase the network's lifetime. Using validity indexes and meta-heuristic algorithms are common ways to improve performance of clustering. In this paper, we provide a new validity index called ASCS by enhancing the Chou and Su (CS) validity index, and improve the performance of the meta-heuristic algorithms for clustering using this new validity index as their objective functions. Differential evolution and harmony search are two algorithms that will be used for this purpose. The experimental results show the better performance of ASCS index comparing to Davies and Bouldin (DB) and CS validity indexes in determining appropriate number of clusters and determining proper clusters' members.

关键词： Clustering Wireless sensor network Validity index meta-heuristic algorithm Performance evaluation

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Investigating hybrid deep learning models and meta-heuristic algorithms in predicting evaporation from a reservoir: a case study of Dez dam

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EARTH SCIENCE INFORMATICS 2023年第4期16卷 3597-3618页

作者： Farzad, Reza Ahmadi, Farshad Sharafati, Ahmad Hosseini, Seyed Abbas Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Shahid Chamran Univ Ahvaz Dept Hydrol & Water Resources Engn Ahvaz Iran

Reservoirs are crucial for water storage, flood control, and electricity generation. At the same time, evaporation in dam reservoirs causes water losses. This study evaluates the effect of weather conditions on the prediction of evaporation from the dam reservoir, and there are several developed models to predict evaporation under different scenarios. Using the deep neural network model long short term memory (LSTM) with a series of modern meta-heuristic algorithms, the present research aims to achieve an acceptable accuracy on weather parameters. The present study investigates the ability of the LSTM deep neural network and meta-heuristic algorithms of Artificial Bee Colony (ABC), Horse Herd Optimization (HOA), and Marine Predators (MPA) to develop an evaporation rate forecasting model for a tropical area in the Dez Dam reservoir, Khuzestan province, Iran. To investigate the effect of different input variable patterns on the prediction accuracy of the case model, twenty-eight scenarios were investigated for the input architecture along with the LSTM model and its combination with meta-heuristic algorithms. The models include ABC-LSTM, LSTM-HOA, and LSTM-MPA, respectively. The results showed that the combined LSTM-MPA model with RMSE, MAE, KGE, and WI evaluation criteria equal to 81.926 mm/month, 53.684 mm/month, 0.850, and 0.816, respectively, performs better than other meta-heuristic models. The examined deep meta-heuristic models are suggested for predicting evaporation from the Dez dam reservoir with limited information because the results show that they have more appropriate prediction capability than the LSTM Model.

关键词： Evaporation prediction Dez dam LSTM model meta-heuristic algorithm

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meta-heuristic algorithms for Nesting Problem of Rectangular Pieces

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Procedia Engineering 2017年 183卷 291-296页

作者： Ernesto Lo Valvo Dipartimento di Architettura Università di Palermo Viale delle Scienze 90128 Palermo Italy

Nesting problems consist of placing multiple items onto larger shapes finding a good arrangement. The goal of the nesting process is to minimize the waste of material. It is common to assume, as in the present work, that the stock sheet has fixed width and infinite height, since in the real world a company may have to cut pieces from a roll of material. The complexity of such problems is often faced with a two-stage approach, so-called “hybrid algorithm”, combining a placement routine and a meta-heuristic algorithm. Starting from a given positioning sequence, the placement routine generates a non-overlapping configuration. The encoded solution is manipulated and modified by the meta-heuristic algorithm to generate a new sequence that brings to a better value of the objective function (in this case the height of the strip). The proposed method consists in placing the rectangles inside a strip and in combining the meta-heuristic algorithms with the No Fit Polygon algorithm. The software has been developed in Python language using proper libraries to solve the meta-heuristic techniques ( Inspyred ) and the geometric problems ( Polygon ). The results show the effectiveness of the proposed method; moreover, with regard to problems reported in literature employed as benchmark of the nesting algorithms, the degree of occupation values (Efficiency Ratio, ER) are shown to be higher than 90%.

关键词： Sheet metal optimisation meta-heuristic algorithm No Fit Polygon algorithm

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A Modified Bat Mechanism for Virtual Machine Migration in a Cloud Environment

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SN Computer Science 2025年第1期6卷 1-13页

作者： Archana Kumar, Narander Department of Computer Science Babasaheb Bhimrao Ambedkar University (A Central University) Vidya Vihar Raibareli Road Uttar Pradesh Lucknow 226025 India

In cloud computing, virtual machines (VMs) must be moved from one overloaded server to another to control impending resource requests and reduce the performance overhead. Hence, the main objective is to carry out the VM migration as effectively as possible by shortening the migration process, lowering the Makespan, and boosting the throughput of the VMs. Here, proposed a modified Bat mechanism that addresses the aforementioned problems and completes the VM migration effectively. The fitness function calculation strategy of spider monkey optimization is added to the standard Bat algorithm inside this proposed mechanism to improve each Bat’s fitness calculation and provide more opportunities to determine the fittest move quickly. The best move enables VMs to connect with the closest PMs (physical machines) and complete migration promptly rather than being stuck. This proposed mechanism overcomes the issue of the standard Bat algorithm that gets stuck in local optima due to inadequate exploration. The fitness function, migration time, makespan, and throughput performance metrics have been used to calculate the effectiveness of the proposed algorithm as compared to the Standard Bat algorithm, PSO (particle swarm optimization), and CS (cuckoo search) algorithm by using the CloudSimPlus simulator. The migration time of the proposed mechanism is 25% faster than that of the standard Bat algorithm, 27% more than PSO, and 35% more than the CS algorithm. It also outperforms other traditional algorithms in makespan, fitness, and throughput. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud environment Makespan meta-heuristic algorithm Migration time Modified Bat algorithm Virtual machine migration

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Namib beetle optimization algorithm: A new meta-heuristic method for feature selection and dimension reduction

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第1期34卷

作者： Chahardoli, Meysam Eraghi, Nafiseh Osati Nazari, Sara Islamic Azad Univ Arak Branch Comp Engn Dept Arak Iran

Today, large amounts of data are generated in various applications such as smart cities and social networks, and their processing requires a lot of time. One of the methods of processing data types and reducing computational time on data is the use of dimension reduction methods. Reducing dimensions is a problem with the optimization approach and meta-heuristic methods can be used to solve it. Namib beetles are an example of intelligent insects and creatures in nature that use an interesting strategy to survive and collect water in the desert. In this article, the behavior of Namib beetles has been used to collect water in the desert to model the Namib beetle optimization (NBO) algorithm. In the second phase of a binary version, this algorithm is used to select features and reduce dimensions. Experiments on CEC functions show that the proposed method has fewer errors than the DE, BBO, SHO, WOA, GOA, and HHO algorithms. In large dimensions such as 200, 500, and 1000 dimensions, the NBO algorithm of meta-heuristic algorithms such as HHO and WOA has a better rank in the optimal calculation of benchmark functions. Experiments show that the proposed algorithm has a greater ability to reduce dimensions and feature selection than similar meta-heuristic algorithms. In 87.5% of the experiments, the proposed method reduces the data space more than other compared methods.

关键词： dimension reduction feature selection meta-heuristic algorithm Namib beetle optimization (NBO) optimization

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A Comparative Study of Various meta-heuristic algorithms for Ab Initio Protein Structure Prediction on 2D Hydrophobic-Polar Model 5th

A Comparative Study of Various Meta-Heuristic Algorithms for...

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5th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Dubey, Sandhya P. N. Balaji, S. Kini, N. Gopalakrishna Kumar, M. Sathish Manipal Univ MIT Dept Comp Sci & Engn Manipal Karnataka India Manipal Univ MIT Dept Biotechnol Manipal Karnataka India Manipal Univ MIT Dept ECE Manipal Karnataka India

ISBN: (纸本)9789811004513;9789811004506

Ab initio protein structure prediction (PSP) models tertiary structures of proteins from its sequence. This is one of the most important and challenging problems in bioinformatics. In the last five decades, many algorithmic approaches have been made to solve the PSP problem. However, it remains unsolvable even for proteins of short sequence. In this review, the reported performances of various meta-heuristic algorithms were compared. Two of the algorithmic settings-protein representation and initialization functions were found to have definite positive influence on the running time and quality of structure. The hybrid of local search and genetic algorithm is recognized to be the best based on the performance. This work provides a chronicle brief on evolution of alternate attempts to solve the PSP problem, and subsequently discusses the merits and demerits of various meta-heuristic approaches to solve the PSP problem.

关键词： Protein structure prediction HP model meta-heuristic algorithm NP-complete Evolutionary algorithm

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Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

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MANAGERIAL FINANCE 2023年第4期49卷 741-757页

作者： Mohammadi, Iman Khoshouei, Hamzeh Mohammadi Chadegani, Arezoo Aghaei Islamic Azad Univ Dept Management Najafabad Branch Najafabad Iran Islamic Azad Univ Dept Accounting Isfahan Khorasgan Branch Esfahan Iran Islamic Azad Univ Dept Accounting Najafabad Branch Najafabad Iran

PurposeIn this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these ***/methodology/approachThe existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets *** results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these *** limitations/implicationsOne of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the

关键词： Price bubble Portfolio optimization meta-heuristic algorithm Returns Risk

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How meta-heuristic algorithms Contribute to Deep Learning in the Hype of Big Data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th International Conference on Advanced Computing, Networking and Informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning meta-heuristic algorithm Neural network training Nature-inspired computing algorithms algorithm design

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