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A multi-objective privacy preservation model for cloud security using hybrid Jaya-based shark smell optimization

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第6期34卷 2343-2358页

作者： Ahamad, Danish Hameed, Shabi Alam Akhtar, Mobin Shaqra Univ Coll Sci & Arts Dept Comp Sci Sajir Saudi Arabia Shaqra Univ Dept Comp Sci Coll Sci & Humanities Huraymla Saudi Arabia Riyadh Elm Univ Dept Basic Sci Riyadh Saudi Arabia

The rising volume of sensitive and personal data being harvested by data controllers has increased the security essentials in the cloud system. The cloud module is not used just to store the data, but also to process them on cloud premises. Security for the cloud premises is essential as the cloud has lot of outsourced, unprotected sensitive data for the public access. This has resulted repeated data violations, and thus there is a need for the advanced legal data protection constraints. Various studies were conducted to adopt the privacy preservation in the cloud, and most of the state-of-the-art techniques fail to handle the optimal privacy when dealing with sensitive data, as it requires separate data sanitization and restoration models. To overcome this challenge, this paper tempts to develop the privacy preservation model in the cloud environment using the advancements of artificial intelligent techniques. Artificial Intelligent capabilities are working in the business cloud computing environment to make organizations more efficient, strategic, and insight-driven. However, by hosting the data, cloud computing offers businesses high flexibility, agility, and cost savings. The two main phases of the proposed privacy preservation system are the data sanitization and restoration. Moreover, the proposed sanitization process depends on the optimal key generation, which is performed by the hybrid meta-heuristic algorithm. This hybrid algorithm merges two well-performed algorithms, such as Shark Smell Optimization (SSO) and Jaya Algorithm (JA), and thus termed as Jaya-based Shark Smell Optimization (J-SSO). The optimal key generation is accomplished by deriving a multi-objective function that involves the parameters, such as the degree of modification, hiding ratio, and information preservation ratio. Finally, the performance analysis has proved the efficiency of the proposed model over the state-of-the-art models in enhancing cloud security. (C) 2022 Published by Else

关键词： Cloud security Privacy preservation Optimal key generation Jaya-based shark smell optimization Hybrid optimization multi-objective function

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Hybrid weight optimized deep learning for autism and visual sensory processing disorder detection: A heuristic strategy on medical domain

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第1期18卷 533-559页

作者： Dedgaonkar, Suruchi Sachdeo, Rajneeshkaur MIT ADT Univ Inst Informat Technol Sch EngnSch Engn Dept Comp Sci & Engn Pune Maharashtra India Inst Informat Technol Fac Vishwakarma Pune Maharashtra India MIT ADT Univ MIT Sch Engn Dept Comp Sci & Engn Pune Maharashtra India

An Autism Spectrum Disorder (ASD) affected individual has several difficulties with social-emotional cues. The existing model is observed with emotional face processing in adolescents and ASD and Typical Development (TD) by utilizing various body parameters. Scanning facial expressions is the initial task, and recognizing the face's sensitivity to different emotional expressions is the next complex task. To address this shortcoming, in this work, a new autism and visual Sensory Processing Disorder (SPD) detection model for supporting healthcare applications by processing facial expressions and sensory data of heart rate and body temperature. Here, initially, the individual data regarding facial emotions and other body parameters like heart rate and body temperature are collected from various subjects. Then, the selection of optimal features is executed by a hybrid algorithm named Density Factor-based Artificial Bee Honey Badger Optimization (DF-ABHBO), where the most essential features are attained and fed to the detection phase. The optimal feature selection is made by resolving the fitness function with constraints like correlation, data variance, and cosine similarity for inter and intra-class. Finally, the autism and visual SPD detection are performed through a Hybrid Weight Optimized Deep Neural Recurrent Network (HWODNRN), where the hyperparameter and weights of "Deep Neural Network (DNN) and Recurrent Neural Network (RNN)" are optimized with the developed DF-ABHBO technique. From the result analysis, the accuracy and F1-score rate of the offered DF-ABHBO-HWODNRN method have attained 96% and 93%. The findings obtained from the simulations of the designed system achieve better performance.

关键词： Autism and visual sensory processing disorder density factor-based artificial bee honey badger optimization hybrid weight optimized deep neural recurrent network multi-objective function optimal feature selection

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Landscape generator: Method to generate landscape configurations for spatial plan-making

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COMPUTERS ENVIRONMENT AND URBAN SYSTEMS 2013年第May期39卷 1-11页

作者： Slager, C. T. J. de Vries, B. Eindhoven Univ Technol Dept Built Environm NL-5612 AZ Eindhoven Netherlands

Current spatial planning tools focus mainly on land use evaluation and not on spatial plan design. Automated generation of spatial design intends to bridge the gap between graphical design and geographical information systems. We propose a new method that generates spatial plans with a high level of detail and realism. A simple set of rules is derived by the modeler from a landscape type that serves as a reference. These rules are implemented by spatial functions with a landscape type specific objective. In a multi-objective optimization algorithm a landscape plan is generated for a specific lot that meets the objectives. The modeler controls the plan generation process through an objective task list which determines the priority of the objectives. Examples show that the landscape generator is capable of generating plausible spatial plans in reasonable computing time. More specifically, they show that the landscape generator performs best with low configurationally structured reference projects and performs less with high configurationally structured reference projects. The manual construction of the objective task list could be further improved by pre-structured objectives dependent on landscape designer's preference. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Spatial plan design Plan generation multi-objective function Heuristic optimization

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multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

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WEB INTELLIGENCE 2023年第4期21卷 385-405页

作者： Reddy, G. Narendrababu Kumar, S. Phani G Narayanamma Inst Technol & Sci Hyderabad 500104 Telangana India GITAM Univ Sch Technol Hyderabad 502329 Telangana India

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation;still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method's performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.

关键词： Security cloud computing workflow scheduling multi-objective function virtual machine

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Stability-aware Energy Efficient Clustering Protocol in WSN using Opposition-based Elephant Herding Optimisation

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Journal of Control and Decision 2022年第2期9卷 202-217页

作者： Y.Alekya Rani E.Sreenivasa Redd Computer Science Engineering Acharya Nagarjuna UniversityGunturIndia

The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in *** paper plans to derive a multi-objective function with the constraints like energy,distance,delay,stability period,and intents to attain the objective by developing a new well-performing meta-heuristic algorithm called Opposition-based Elephant Herding Optimisa-tion(O-EHO).The objective function diminishes the energy consumption of sensor nodes by optimum selection of cluster heads that leads to maintain the energy balance between the nor-mal *** this way,there is a remarkable enhancement in the performance parameters such as throughput,stability period,and network *** is proved that the network lifetime is enhanced by the stability period and thus it is considered as the most significant *** experimental analysis proves the competitive performance of the proposed model over other heuristic methods.

关键词： Wireless sensor network network stability energy delay clustering cluster head selection fitness updated elephant herding optimisation multi-objective function

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Incorporating quantum key distribution and reinforcement learning for secure and efficient smart grid advanced metering infrastructure

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OPTICAL AND QUANTUM ELECTRONICS 2024年第6期56卷 932-932页

作者： Beula, G. Starlin Franklin, S. Wilfred Arunachala Coll Engn Women Elect & Commun Engn Nagercoil Tamil Nadu India CSI Inst Technol Elect & Commun Engn Kanyakumari Tamil Nadu India

The Smart Grid AMI is at the forefront of modernizing energy management for efficient energy distribution and maintaining grid reliability. However, the seamless flow of data within AMI introduces critical security challenges, necessitating innovative approaches to ensure secure data transmission. This paper addresses these challenges by proposing a Quantum Key Distribution (QKD) based on multi-objective Optimization (MOO) and Reinforcement Learning (RL) framework for secure data transmission within the Smart Grid AMI. Primarily, we formulate the routing problem as a MOO challenge with four primary objectives namely, energy efficiency, latency minimization, reliability, and security to capture the essence of efficient and secure data transmission. Further, we introduce an RL agent based on the Proximal Policy Optimization (PPO) algorithm for robust policy learning. The RL agent by exploring diverse routing actions and receiving rewards optimizes the routing decisions within the network environment based on the objectives specified in the multi-objective function (MOF). A novel MOF quantifies trade-offs between security and performance metrics, integrating QKD-based security metrics with traditional optimization objectives. This function guides the RL agent in making informed routing decisions. Through extensive simulations using the NS-2 simulator in a realistic Smart Grid AMI environment, the proposed approach obtained energy consumption of 750 J, latency of 55 ms, and security level of 96% and also revealed significant improvements compared to conventional techniques widely employed for secured communication in AMI networks. Overall, the simulation results exhibited that the proposed method showed outstanding performance by achieving energy efficiency of 0.95, latency reduction of 0.92, reliability improvement of 0.94, and security enhancement of 0.96. Overall, this integration offers a pioneering solution to address the evolving security challenges in the Smart G

关键词： Smart grid AMI Security QKD Data transmission multi-objective function

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Three-phase balancing of distribution feeders using immune algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2008年第3期2卷 383-392页

作者： Huang, M. -Y. Chen, C. -S. Lin, C. -H. Kang, M. -S. Chuang, H. -J. Huang, C. -W. Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung 80424 Taiwan Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Kao Yuan Univ Dept Elect Engn Lu Chu Taiwan Taiwan Power Co Power Res Inst Taipei Taiwan

The immune algorithm (IA) is proposed to derive the rephasing strategy arrangement of laterals and distribution transformers to enhance three-phase balancing of distribution systems. The multi-objective function is formulated by considering the unbalance of phasing currents, the customer service interruption cost (CIC) and labour cost to perform the optimal rephasing strategy. For each feasible rephasing strategy, the number of customers affected with total interruption load demand and outage duration time are used to calculate the impact of system reliability because of rephasing engineering works. To demonstrate the effectiveness of the proposed methodology, a practical distribution feeder in Taipower with 271 customers is selected for computer simulation. By minimising the objective function subjected to the operation constraints, the rephasing strategy has been derived by selecting the laterals and distribution transformers for phasing adjustment. It is found that the neutral current of test feeder has been reduced to be less than the neutral overcurrent limit by executing the rephasing of laterals and distribution transformers.

关键词： customer service interruption cost labour cost immune algorithm multi-objective function system reliability Distribution networks distribution transformers Transformers and reactors power distribution reliability distribution feeders three-phase balancing power transformers rephasing strategy arrangement

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Minimization of Operational and Seepage Losses in Agricultural Water Distribution Systems Using the Ant Colony Optimization

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WATER RESOURCES MANAGEMENT 2021年第3期35卷 827-846页

作者： Azargashb Lord, Sara Hashemy Shahdany, Seied Mehdy Roozbahani, Abbas Univ Tehran Dept Water Engn Coll Aburaihan Tehran Iran

The present study's main objective is to simultaneously minimize operational and seepage losses in agricultural water distribution systems, relying on the Ant Colony Optimization (ACO). For this purpose, the following arrangements were made: i) Hydraulic flow simulation of the distribution systems was conducted by developing an Integrator Delay (ID) model using MATLAB and appraisal performance of the water distribution system, ii) The seepage simulation alongside the system employing a calibrated and validated estimation equation, iii) developing the ACO model to minimize operational and seepage losses within the agricultural water distribution Systems. Two single-objective and one multi-objective functions were considered to minimize seepage loss, operational loss, and both loss components simultaneously. The Moghan irrigation water distribution system, Iran, was selected as the case study. Optimization results revealed that the first through third objective functions managed to reduce the total losses in the Moghan water distribution systems by, respectively, 0.39, 3.1, and 4% compared to the existing conditions. A comparison with the optimization results from LINGO, a nonlinear optimization model, was suggestive of the advantages of the ACO in terms of the optimal result and optimization time. The proposed method can be used as a practical measure to improve water productivity within the scale of agricultural water distribution systems by improving the manual operating system's performance in the status quo.

关键词： Seepage modeling Hydraulic flow simulation Losses minimization multi-objective function Ant Colony algorithm

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Hierarchical Agglomerative Clustering Algorithm method for distributed generation planning

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第Mar.期56卷 259-269页

作者： Vinothkumar, K. Selvan, M. P. ABB Global Ind & Serv Ltd Madras 600089 Tamil Nadu India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 Tamil Nadu India

Planning of distributed generator (DG) units in the distribution system employing Hierarchical Agglomerative Clustering Algorithm (HACA) is proposed in this paper. The proposed method overcomes the dependency of existing methods of DG placement, either on the entire global preference information or on the experience of the distribution system planner. The proposed method is validated with weighted sum method and its effectiveness is tested using two distribution systems of different size and configuration. The results of the simulation study demonstrate the suitability of proposed method in solving the problem involving multiple objectives in DG planning studies. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Distributed generator (DG) planning Performance indices multi-objective function Hierarchical Agglomerative Clustering Algorithm (HACA)

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A land-use mix allocation model considering adjacency, intensity, and proximity

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2020年第5期34卷 899-923页

作者： Eom, Sunyong Suzuki, Tsutomu Lee, Myeong-Hun Univ Tokyo Ctr Spatial Informat Sci Kashiwa Chiba Japan Univ Tsukuba Fac Engn Informat & Syst Div Policy & Planning Sci Tsukuba Ibaraki Japan Hanyang Univ Grad Sch Urban Studies Seoul South Korea

Land-Use Mix (LUM) refers to the strategy of integrating complementary functions within a building or area. While LUM has become a dominant approach in urban planning, its actual benefits and vision for spatial planning remain unclear. To clarify this issue, this study discerns the spatial features of land-use patterns depending on the compatibilities among land-use categories. Accordingly, this study introduces three LUM measures - adjacency, intensity, and proximity - to identify differences in the spatial distribution of land-use categories. Based on these measures, a land-use allocation model is developed to specify spatial patterns satisfying the given compatibilities. This model is tested by applying the concept of the neighborhood unit on a case study of normative land-use patterns subject to specified compatibilities. The results describe spatial features of four compatibility sets, including a set exhibiting a compatibility conflict between the same land-use pair and LUM measures when, for example, a given land-use pair is compatible in terms of intensity but incompatible in terms of proximity. Understanding the spatial features of a normative land-use pattern that satisfies various possible compatibilities will facilitate the incorporation of the LUM approach into local planning guidance and zoning ordinances.

关键词： Land-use allocation land-use mix multi-objective function spatial planning

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