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A Discrete Adaptive Lion Optimization algorithm for QoS-Driven IoT Service Composition with Global Constraints

JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2024年第2期32卷 34-34页

作者： Ouhadda, Souhila Ait Hacene Sadouki, Samia Chibani Achroufene, Achour Tari, Abdelkamel Univ Bejaia Fac Exact Sci LIMED Lab Bejaia 06000 Algeria Univ Bejaia Fac Technol Bejaia 06000 Algeria Ecole Super Sci & Technol Informat & Numer Bejaia 06300 Algeria

In an Internet of Things (IoT) environment, multiple objects usually interact with one another to meet a complex user's request. This involves the composition of several atomic IoT services. Given a large number of functionally equivalent services with different Quality of Service (QoS) values, the service composition problem remains one of the main challenges in IoT environments. This paper presents a Discrete Adaptive Lion Optimization algorithm (DALOA) to select IoT services in a composition process while considering global user QoS constraints. DALOA is based on the Lion Optimization algorithm (LOA) and developed by combining several LOA operators, such as roaming, mating, and migration. First, DALOA divides the initial population into two sub-populations: pride and nomad, and each sub-population has its search strategies. Second, the roaming nomad process follows a random searching mode (strong exploration) to avoid being trapped in local optima. Third, the roaming pride searching mode represents strong local research, ensuring more efficient exploitation. Four, mating (mating pride, mating nomad) allows for information sharing between members of the same population. Finally, the migration operator is used to ensure population diversity by allowing information sharing between the pride and the nomad. The simulation results show that DALOA obtains the best compositional optimality and finds the near-optimal composition of the IoT services in a reasonable execution time compared to other approaches. Indeed, the combination of the previous operators provides a good trade-off between exploration and exploitation.

关键词： bio-inspired algorithm Service composition Internet of Things Lion Optimization algorithm (LOA) Discrete Adaptive LOA Quality of Service

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Modelling and optimizing secondary metabolites production in Spirodela polyrhiza using machine learning

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JOURNAL OF CLEANER PRODUCTION 2024年 478卷

作者： Tan, Win Hung Tong, C. Y. Chua, M. X. Derek, C. J. C. Univ Sains Malaysia Sch Chem Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia

Spirodela. polyrhiza as one of the potential feedstocks for industrial natural dye and pharmaceutical products, efficient process to produce from it were scarce. Here, Artificial Neural Network (ANN) and Genetic algorithm (GA) were employed for modelling and optimization respectively. Current study focus on establishing useable models and anticipating the optimal concentration of nitrate (NO3-), phosphate (PO43-) and ammonium (NH4+) to give highest relative growth rate (RGR) and produce maximum amount of anthocyanins, chlorophylls, phenolic compounds and antioxidants. In short, 12 and 11 neurons in hidden layer were found to be most suitable neuron numbers used for the modelling of growth and secondary metabolites respectively. All the models exhibited a high degree of robustness and performance with a high r(2) value (higher than 0.75) and a low mean squared error (lower than 0.05). GA also anticipated the best concentration of macronutrients to achieve the maximum growth and metabolites production with significant low errors (below 7 %). The employment of machine learning was useful for such applications.

关键词： Antioxidants Plant Anthocyanins Software bio-inspired algorithm

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PANDA: A physarum-inspired algorithm to solve the multi-objective discrete network design problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Heeroo, Kelvin Nigel Shinday Gukhool, Oomesh Univ Mauritius Fac Informat Commun & Digital Technol Dept Software & Informat Syst Reduit Mauritius Univ Mauritius Fac Informat Commun & Digital Technol Dept Software & Informat Syst Reduit Mauritius

The Multi -Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin-destination pair instead of shared arterials.

关键词： Physarum Polycephalum bio-inspired algorithm Network design problem

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Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

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Computers, Materials & Continua 2021年第3期66卷 2555-2571页

作者： Phong Thanh Nguyen Vy Dang Bich Huynh Khoa Dang Vo Phuong Thanh Phan Mohamed Elhoseny Dac-Nhuong Le Department of Project Management Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Department of Learning Material Ho Chi Minh City Open UniversityHo Chi Minh City700000Vietnam Faculty of Computers and Information Mansoura UniversityDakahlia Governorate35516Egypt Institute of Research and Development Duy Tan UniversityDanang550000Vietnam Faculty of Information Technology Duy Tan UniversityDanang550000Vietnam

Data fusion is a multidisciplinary research area that involves different *** is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare *** generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become *** medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems(IDS).In this regard,since singularmodality is not adequate to attain high detection rate,there is a need exists to merge diverse techniques using decision-based multimodal fusion *** this view,this research article presents a new multimodal fusion-based IDS to secure the healthcare data using *** proposed model involves decision-based fusion model which has different processes such as initialization,pre-processing,Feature Selection(FS)and multimodal classification for effective detection of *** FS process,a chaotic Butterfly Optimization(BO)algorithmcalled CBOA is *** the classic BO algorithm offers effective exploration,it fails in achieving faster *** order to overcome this,i.e.,to improve the convergence rate,this research work modifies the required parameters of BO algorithm using chaos ***,to detect intrusions,multimodal classifier is applied by incorporating three Deep Learning(DL)-based classification ***,the concepts like Hadoop MapReduce and Spark were also utilized in this study to achieve faster computation of big data in parallel computation *** validate the outcome of the presented model,a series of experimentations was performed using the benchmark NSLKDDCup99 Dataset *** proposed model demonstrated its effective results on the applied dataset by offering the maximum accuracy of 99.21%,precision of 98.93%and detection rate of 99.59%.The results assured the betterm

关键词： Big data data fusion deep learning intrusion detection bio-inspired algorithm spark

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Enhanced swarm intelligence optimization: inspired by cellular coordination in immune systems

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KNOWLEDGE-BASED SYSTEMS 2024年 290卷

作者： Liu, Bao Xu, Mei Gao, Lei China Univ Petr Coll Control Sci & Engn Qingdao 266580 Peoples R China Qingdao Haixi Heavy Duty Machinery CO LTD HHMC Qingdao 266530 Peoples R China Commonwealth Sci & Ind Res Org CSIRO Waite Campus Urrbrae SA 5064 Australia Commonwealth Sci & Ind Res Org CSIRO Private Mail Bag 2Waite Rd Glen Osmond SA 5064 Australia

Swarm intelligence optimization algorithms (SIOAs) are widely used to address complex problems but often trapped in local optima. To overcome this, we propose a novel multi-population SIOA based on cellular coordination mechanisms in immune systems (referred as CCOA), inspired by the immune system's efficient elimination of viruses. The CCOA consists of four key units: an intra-population cell communication unit, an interpopulation cell communication unit, a cell migration unit, and a cell division unit. Through collaborative communication, the intra-population and inter-population units guide cells towards global optima, enhancing the algorithm's global search ability. The cell migration unit improves convergence speed by guiding cell movements. The cell division unit allows cells to divide in large numbers during the late iterations for improved convergence accuracy. We evaluate the effectiveness of the CCOA using eight standard test functions and apply it to river flow prediction in an Elman neural network (referred as CCOA-Elman). The results obtained from optimizing test functions demonstrate that the CCOA outperforms existing algorithms in accuracy, convergence, and stability. The real-world application illustrates that the CCOA-Elman achieves over 95% prediction accuracy, surpassing the accuracies of three other models. The CCOA offers a promising approach to overcome local optima in optimization.

关键词： bio-inspired algorithm Optimization Multi -population swarm intelligence Elman neural network Immune system River flow prediction

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Ant Colony Optimization for Feature Selection via a Filter-Randomized Search Heuristic 22

Ant Colony Optimization for Feature Selection via a Filter-R...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ortega, Alberto Escobar, Juan Jose Damas, Miguel Ortiz, Andres Gonzalez, Jesus Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain Univ Malaga Commun Engn Dept Malaga Spain Andalusian Data Sci & Computat Intelligence Inst Granada Spain

ISBN: (纸本)9781450392686

The combination of bio-inspired algorithms with the design of new search heuristics has allowed the development of innovative methods for combinatorial problems where the number of possible solutions is unaffordable. However, for years swarm intelligent methods have proven to be adequate to deal with these issues. Therefore, this paper proposes a new application of the Ant Colony Optimization (ACO) algorithm for Feature Selection (FS). Specifically, the proposed algorithm applies ACO for FS using a filter approach as a heuristic and makes use of a randomized search, resulting in a very innovative algorithm for variable reduction.

关键词： Ant Colony Optimization Feature Selection bio-inspired algorithm Swarm Intelligence Randomized Search Heuristic

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Phototropic algorithm for global optimisation problems

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APPLIED INTELLIGENCE 2021年第8期51卷 5965-5977页

作者： Chandra S. S., Vinod Hareendran S., Anand Univ Kerala Dept Comp Sci Thiruvananthapuram Kerala India Muthoot Inst Technol & Sci Dept Comp Sci & Engn Kochi India

Problem solving and decision-making have a vital role to play in both technical and non-technical fields. Some decisions are simple while others require more effort and time to solve. This article introduces a new problem solving technique called Phototropic optimization algorithm, inspired from the optimised growth pattern in plants. It has been observed that the stem tips of a plant always grow towards sunlight. In this algorithm, the underlying hormonal mechanism of phototropism is emulated to solve computational problems. This phenomenon has indicated strong prospects of algorithmic efficiency and invites further research into prospective computational applications. Phototropic algorithm is developed as an optimization technique to solve real time application such as shortest path finding problems, travelling salesman problem, finding congestion in a network or any similar problem seen around. A prototype on finding the minimal distance between any two nodes in the physical network is modelled here. The asymptotic time complexity analysis shows the algorithm routes packages in O (n log n). Comparison with the traditional algorithms gives sufficient evidence for the efficiency of this proposal. This can be implemented over Software Defined Networks (SDN) for increasing system capabilities in route analytics and functionalities. Extension of this optimization algorithm is useful to solve various real time problems.

关键词： bio-inspired algorithm Congestion based routing Optimising model Phototropic algorithm Shortest path

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Grey Wolf optimization-Elman neural network model for stock price prediction

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SOFT COMPUTING 2021年第1期25卷 649-658页

作者： Kumar Chandar, S. CHRIST Deemed Univ Sch Business & Management Bangalore Karnataka India

Over the past two decades, assessing future price of stock market has been a very active area of research in financial world. Stock price always fluctuates due to many variables. Thus, an accurate prediction of stock price can be considered as a tough task. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. Trial and error-based method is widely used to determine the parameters of ENN. It is a time-consuming task. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN. Optimized ENN is utilized to predict the future price of stock data in 1 day advance. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data. The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman, CSO-ARMA and GA-LSTM to prove its superiority. Results demonstrated that the GWO-ENN model provides accurate prediction for 1 day ahead prediction and outperforms the benchmark models taken for comparison.

关键词： Average relative variance bio-inspired algorithm Elman neural network Grey Wolf optimization Stock prediction

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bio-inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

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IEEE ACCESS 2021年 9卷 18649-18669页

作者： Arafat, Muhammad Yeasir Moh, Sangman Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.

关键词： Location awareness Clustering algorithms Global Positioning System Fires Energy efficiency Routing Monitoring bio-inspired algorithm cluster head clustering energy efficiency gray wolf optimization localization network lifetime routing protocol unmanned aerial vehicle network

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bio-inspired Optimization-Based Path Planning algorithms in Unmanned Aerial Vehicles: A Survey

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SENSORS 2023年第6期23卷 3051-3051页

作者： Poudel, Sabitri Arafat, Muhammad Yeasir Moh, Sangman Chosun Univ Dept Comp Engn 309 Pilmun daero Gwangju 61452 South Korea

Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

关键词： bio-inspired algorithm optimization algorithm path planning unmanned aerial vehicle UAV communication UAV path planning

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