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A novel two-stage bio-inspired method using red deer algorithm for data clustering

EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1819-1836页

作者： Moghadam, Pourya Ahmadi, Abbas Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Polytech 424 Hafez Ave Tehran Iran

Data clustering is a method of dividing data points into similar groups. In the last few decades, there have been numerous advancements in algorithms and methodologies for the analysis of clustered data. Although some algorithms like K-means, due to their easy implementation and interpretability, are widely used for clustering, their capability of providing good solutions might be sensitive to the number of dimensions and clusters. Due to the advantages of metaheuristic-based clustering approaches like strong searchability, they have been widely used to tackle the clustering problem. Based on this premise, we propose a new bio-inspired red deer clustering algorithm (RDCA). red deer algorithm (RDA) is a new metaheuristic inspired by the behavior of the Scottish red deer during a breeding season. First, the data space is partitioned into several divisions, each of which is represented by a disjunctive normal form expression using CLIQUE algorithm. Then a search strategy employing a modified version of RDA is pursued. Experiments are carried out to assess the performance of RDCA in comparison to eight traditional methods using six real-world and three generated datasets in terms of cluster validity measures, namely compactness, separation, combined, Turi, Davies Bouldin, and normalized mutual information score. The proposed method can handle multiple objective functions simultaneously and shows good performance when the data dimension is large. The results indicate that in most scenarios, the proposed method outperforms its counterparts. It is also shown that the proposed algorithm is less sensitive to increasing the number of clusters and dimensions.

关键词： Data clustering red deer algorithm Evolutionary-based clustering Grid-based clustering RDA-based clustering Unsupervised learning

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Automated red deer algorithm with Deep Learning Enabled Hyperspectral Image Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

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red deer algorithm Based Harmonic Mitigation for Asymmetric Cascaded Multilevel Inverters 57

Red Deer Algorithm Based Harmonic Mitigation for Asymmetric ...

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57th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST)

作者： Bektas, Yasin Karaca, Hulusi Univ Aksaray Dept Elect Energy TR-68100 Aksaray Turkey Selcuk Univ Technol Fac Dept Elect & Elect Engn TR-42100 Konya Turkey

ISBN: (纸本)9781665485005

This study aims to find the optimum switching angles in an asymmetric cascaded H-bridge multilevel inverter (CHB-MLI) powered by photovoltaic (PV) panels. red deer algorithm (RDA), a newly developed algorithm, has been applied to solve the selective harmonic elimination (SHE) equations. The calculated switching angles are used to obtain a 19-level output voltage using only 3 different amplitude DC sources and 3 H-bridge modules in Matlab/Simulink. The simulation results show that the RDA can accurately and quickly suppress selected harmonics while controlling the fundamental voltage with less than 0.06% error in the modulation index range of 0.2 to 1.0. The proposed RDA optimization has eliminated the selected harmonics without complex analytical calculations and reduced the total harmonic distortion.

关键词： Cascaded Multilevel Inverters Asymmetric Inverters Selective Harmonic Elimination red deer algorithm

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red deer algorithm based nano-sensor node clustering for IoNT

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2023年第1期213卷

作者： Gulec, Omer Sahin, Emre Pamukkale Univ Dept Management Informat Syst Denizli Turkiye

Node connectivity is a crucial issue for collecting and transmitting the values of the medium at nano-scale in Wireless Nano-Sensor Networks (WNSNs). This provides the network to be online when the sensor nodes maintain connectivity among themselves. Therefore, efficient connectivity methods need to be developed for permanent connection on the network. The basic way to provide connectivity over the network is clustering -based solutions which build efficient routing paths between the nodes. On the other hand, meta-heuristics are prominent methods to be preferred in order to select special nodes as cluster heads. In this paper, a modern meta-heuristic method, the red deer algorithm (RDA) based distributed nano-sensor node clustering algorithm, namely nanoRDA, is proposed in order to provide efficient communication between nano-sensor nodes by selecting cluster heads on WNSNs in Internet of Nano-Things (IoNT) applications. nanoRDA is compared with two different Genetic algorithm (GA) setups. According to the results, nanoRDA covers all non-cluster head nodes up to 99.38% on average all over the nano-scale networks while selecting fewer cluster heads than GA-based solutions.

关键词： Internet of Nano-Things Wireless nano-sensor networks red deer algorithm Clustering Coverage Metaheuristics

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Adaptive memory red deer algorithm for cross-dock truck scheduling with products time window

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ENGINEERING COMPUTATIONS 2021年第8期38卷 3254-3289页

作者： Zhou, Binghai Zong, Shi Tongji Univ Sch Mech Engn Shanghai Peoples R China

Purpose - The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks. Design/methodology/approach - This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time. Findings - Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product's time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window. Research limitations/implications - The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other lo

关键词： Scheduling Cross-docking Adaptive memory programming Lagrangian relaxation algorithm red deer algorithm

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Review and analysis for the red deer algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第7期14卷 8375-8385页

作者： Abu Zitar, Raed Abualigah, Laith Al-Dmour, Nidal A. Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci Pulau 11800 Pinang Malaysia Mutah Univ Dept Comp Engn Mutah Jordan

In this paper, the red deer algorithm (RDA), a recent population-based meta-heuristic algorithm, is thoroughly reviewed. The RD algorithm combines the survival of the fittest principle from the evolutionary algorithms and the productivity and richness of heuristic search techniques. Different variants and hybrids of this algorithm are presented and investigated. All the applications that were solved with this algorithm are presented. It is crucial to analyze the performance of this algorithm, therefore, the paper sheds light on the algorithm unique features and weaknesses covering the applications that are primarily suitable for it. The conclusions are presented, and further recommendations are suggested based on the review and analysis covered. The readers of this paper will have an understanding of the RD algorithm and its variants and, consequently, decide how suitable this algorithm is for their own business, research, or industrial applications.

关键词： red deer algorithm Meta-heuristics Evolutionary Productivity Exploitation

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red deer algorithm (RDA): a new nature-inspired meta-heuristic

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SOFT COMPUTING 2020年第19期24卷 14637-14665页

作者： Fathollahi-Fard, Amir Mohammad Hajiaghaei-Keshteli, Mostafa Tavakkoli-Moghaddam, Reza Univ Sci & Technol Mazandaran Dept Ind Engn Behshahr Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran LCFC Arts & Metiers Paris Tech Metz France

Nature has been considered as an inspiration of several recent meta-heuristic algorithms. This paper firstly studies and mimics the behavior of Scottish red deer in order to develop a new nature-inspired algorithm. The main inspiration of this meta-heuristic algorithm is to originate from an unusual mating behavior of Scottish red deer in a breading season. Similar to other population-based meta-heuristics, the red deer algorithm (RDA) starts with an initial population called red deers (RDs). They are divided into two types: hinds and male RDs. Besides, a harem is a group of female RDs. The general steps of this evolutionary algorithm are considered by the competition of male RDs to get the harem with more hinds via roaring and fighting behaviors. By solving 12 benchmark functions and important engineering as well as multi-objective optimization problems, the superiority of the proposed RDA shows in comparison with other well-known and recent meta-heuristics. [GRAPHICS]

关键词： red deer algorithm Meta-heuristics Real-world applications Multi-objective optimization

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Multilevel Image Segmentation Using Modified red deer algorithm 11

Multilevel Image Segmentation Using Modified Red Deer Algori...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Dey, Sandip De, Sourav Deb, Abhirup Debnath, Soumyaratna Sukanta Mahavidyalaya Dept Comp Sci Dhupguri Jalpaiguri India Cooch Behar Govt Engg Coll Dept CSE Cooch Behar India

ISBN: (纸本)9781665414517

In this paper, an evolution strategy based, Modified red deer algorithm (MRDA) has been proposed to find optimum thresholds of publicly available gray scale images. In the recent year, the activity of red deers has been thoroughly observed in the course of their breading season by a group of researchers. Later, this inspired them to introduce a renowned meta-heuristic, popularly known as red deer algorithm (RDA). The RDA is capable to handle several combinatorial optimization problems of various real-world applications. In this paper, a number of adaptive strategy has been suggested to alter the intrinsic operators and parameters used in RDA to improve its performance. The efficiency of MRDA has been judged with RDA and classical Particle Swarm Optimization (PSO) using two publicly accessible real world benchmark images. The performance of each of the competitive method has been analysed with regards to a variety of measure quantitatively and visually. Finally, as a statistical comparison, Kruskal-Wallis test has been carried out between the proposed method and others. The obtained results prove that MRDA is the best performing method than others in all aspects and provides immensely competitive results.

关键词： red deer algorithm Particle Swarm Optimization optimization multi-level image thresholding Kruskal-Wallis test

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Solving Truss Structures Problem by Size Optimizing using red deer algorithm

Solving Truss Structures Problem by Size Optimizing using Re...

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Palestinian International Conference on Information and Communication Technology (PICICT)

作者： Dalbah, Lamees Mohammad Al-Betar, Mohammed Azmi Alshamsi, Hend Salem Awadallah, Mohammed A. Ajman Univ Dept Informat Technol Coll Engn & Informat Technol Ajman U Arab Emirates Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates

ISBN: (纸本)9781665436519

This paper presents an approach to minimize the truss structures weights with discrete and continuous variables using red deer algorithm (RDA). The RDA is one of the most recent meta-heuristic algorithms presented to solve optimization problems in 2016 by Amir and Mostafa. This algorithm mimics the breeding season of Scottish red deer animal, starting from roaring and ending with mating. In this paper, the RDA has been evaluated on three truss structure problems and has been compared with other six meta-heuristic algorithms. The results conclude that the RDA had a good performance for the 10-bar truss structure, and could enhance the compatibility equivalence constraints to minimize the weight of the bar design, and accelerate the convergence rate effectively. On the other hand, the study demonstrates that the RDA algorithm was effectively used to achieve a good enough solution for the 25-bar problem, and ranked as the fifth algorithm for the 72-bar truss structure.

关键词： red deer algorithm metaheuristics optimization truss structures

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Application of red deer algorithm in Optimizing Complex functions 14

Application of Red Deer Algorithm in Optimizing Complex func...

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14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Abu Zitar, Raed Abualigah, Laith Sorbonne Univ Abu Dhabi Sorbonne Ctr Artificial Intelligence Abu Dhabi U Arab Emirates Amman Arab Univ Fac Comp Sci & Informat Amman Jordan

ISBN: (纸本)9781665400046

The red deer algorithm (RDA), a recently developed population-based meta-heuristic algorithm, is examined in this paper with the optimization task of complex functions. The RD algorithm blends evolutionary algorithms' survival of the fittest concept with heuristic search techniques' productivity and richness. It is critical to assess this algorithm's performance in comparison with other well-known heuristic methods. The findings are presented along with additional recommendations for increasing RDA performance based on the analysis. The readers of this paper will gain a grasp of the RD algorithm and its optimization ability to determine whether this algorithm is appropriate for their particular business, research, or industrial needs.

关键词： red deer algorithm Meta-heuristics Evolutionary Productivity Exploitation

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