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An obstacle aware efficient MANET routing with optimized Bi-LSTM and multi-objective constraints on improved heuristic algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2023年第4期15卷 357-380页

作者： Bhavadharini, R. M. Beaulah, P. Mercy Rajaselvi Kumar, C. U. Om Krithiga, R. Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

Mobile Ad Hoc Networks (MANETs) are self-organizing, self-configuring, and infrastructure-less networks for performing multi-hop communication. The source mobile node can transmit the information to any other destination node, but it has limitations with energy consumption and battery lifetime. Since it appeals to a huge environment, there is a probability of obstacle present. Thus, the network requires finding the obstacles to evade performance degradation and also enhance the routing efficiency. To achieve this, an obstacle-aware efficient routing using a heuristic-based deep learning model is proposed in this paper. Firstly, the nodes in the MANET are employed for initiating the transmission. Further, it is needed to be predicted whether the node is malicious or not. Consequently, the prediction for link connection between the nodes is achieved by the Optimized Bi-directional Long-Short Term Memory (OBi-LSTM), where the hyperparameters are tuned by the Adaptive Horse Herd Optimization (AHHO) algorithm. Secondly, once the links are secured from the obstacle, it is undergone for routing purpose. Routing is generally used to transmit data or packets from one place to another. To attain better routing, various objective constraints like delay, distance, path availability, transmission power, and several interferences are used for deriving a multi-objective function, in which the optimal path is obtained through the AHHO algorithm. Finally, the simulation results of the proposed model ensure to yield efficient multipath routing by accurately identifying the intruder present in the network. Thus, the proposed model aims to reduce the objectives like delay, distance, and power consumption.

关键词： Mobile ad hoc networks obstacle-aware routing link connectivity prediction adaptive horse herd optimization algorithm optimized bi-directional long-short term memory optimal routing multi-objective constraints

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An FTSA Trajectory Elliptical Homotopy for Unmanned Vehicles Path Planning With multi-objective constraints

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2415-2425页

作者： Fu, Jinyu Yao, Weiran Sun, Guanghui Tian, Haoyu Wu, Ligang Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

This article investigates a fixed-time simultaneous arrival (FTSA) problem in terms of the equilibrium of path lengths of unmanned vehicles. A novel trajectory elliptical homotopy method (TEHM) is designed to solve the FTSA problem of unmanned vehicles in a multi-objective constrained environment. Considering the constraints of obstacle avoidance and kinematics of unmanned vehicles, the trajectories elliptical homotopy is selected for path planning. The obtained trajectory homotopy guarantees obstacle avoidance and motion stability at the same time. To handle the non-cooperative and dynamic obstacle avoidance, a trajectory elliptical homotopy decomposition (TEHD) algorithm is proposed with an FTSA constraint. Based on the TEHM and TEHD, a multiple unmanned vehicle fixed-time regular-triangle formation algorithm is designed and implemented on real vehicles. Simulations and experiments validate the performance of the proposed methods and show how fixed-time arrival formation under obstacles and kinematic constraints was obtained.

关键词： Trajectory Autonomous vehicles Planning Kinematics Turning Heuristic algorithms Collision avoidance Fixed-time simultaneous arrival elliptical homotopy trajectory decomposition multi-objective constraints

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A Hybrid A* Path Planning Algorithm Based on multi-objective constraints

A Hybrid A* Path Planning Algorithm Based on Multi-objective...

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Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)

作者： Zhao, Yu Zhu, Yongqiang Zhang, Pingxia Gao, Qi Han, Xue Qingdao Univ Technol Sch Mech & Automot Engn Qingdao Peoples R China

ISBN: (数字)9781665451536

ISBN: (纸本)9781665451536

To provide a safe, smooth and efficient global planning path for nonholonomic mobile robots. Aiming at the problems of traditional hybrid A* algorithm in path planning, such as approaching obstacles, unnecessary reversing and redundant turning, a multi-objective constraint method based on hybrid A* algorithm is proposed To speed up the path planning, the heuristic function is dynamically weighted and the overall path cost function is designed The path planning experiments are carried out in ROS (Robot Operation System) simulation environment and actual environment respectively, and the results show that. The hybrid A* algorithm with multi-objective constraints increases the minimum distance between the robot and obstacles by more than 50%, reduces the unnecessary times of reversing and turning, and reduces the total running time by 14.2% on average, thus improving the navigation efficiency of the mobile robot.

关键词： nonholonomic constrained robot multi-objective constraints hybird A* path planning

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Exploring the Optimal Allocation Decision-Making of Expenditure Budget in Hospitals Under multi-objective constraints: Evidence from Urban Public Hospitals, China

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SAGE OPEN 2021年第4期11卷

作者： Ren, Weicun Zhao, Yizhen Zhong, Huiliang Fu, Xiaoli Wu, Jian Xinxiang Med Univ Sanquan Coll Urumqi Peoples R China Zhengzhou Univ Zhengzhou Henan Peoples R China Luoyang Zhenggu Orthoped Hosp Henan Prov Luoyang Peoples R China Henan Nursing Vocat Coll Anyang Peoples R China

Hospitals in many countries face the need for balancing different categories of expenditures to achieve multiple goals within a limited budget. This study established a two-stage fuzzy linear programming (FLP) estimation model to explore the optimal allocation decision-making of expenditure budget under the multi-objective constraints. Taking all urban public hospitals in Henan province of China as a sample, the optimal allocation decision-making of total expenditure budget was tested with the human resources expenditures (HE) as the dependent variable. And the outcome was compared with the actual expenditure data of these hospitals between 2010 and 2016. The study found that when the HE achieves the maximum and minimum feasible scale, the expenditure scales of the budget allocation categories including pharmaceutical expenditures, medical supplies expenditures, and other expenditures were all within a reasonable range. Among them, the observed promoting space for HE was 3.78 billion yuan. The results show that the FLP method can help urban public hospitals to make better total expenditure budget allocation decisions, which can maintain their reasonable expenditure structure under the hospitals' development goals and the government's regulatory requirements.

关键词： FLP hospital expenditure decision-making multi-objective constraints

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Unmanned aerial vehicle for waste management system using genetic algorithms

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Arab Journal of Basic and Applied Sciences 2025年第1期32卷 57-80页

作者： Hejres, Shaima Mahjoub, Amine Hewahi, Nabil College of Information Technology University of Bahrain Zallaq Bahrain

Waste management system is an important economic sector aiding in supporting health and improving the quality of life in smart cities. This paper proposes a multiobjective genetic algorithm (GA) aiming at improving the waste collection process through identifying the best appropriate tours that can minimize the cost. The proposed system aims to satisfy multi-objective approach addressing constraints with a single Unmanned Aerial Vehicle (UAV). It aims at improving performance by reducing flight distance and power consumption while maximizing delivered waste capacity. It incorporates several modifications to the Genetic Algorithm (GA) such as adding distance minimization, energy conservation and capacity maximization mechanisms in the random initial generation, customizing the fitness function and using a hybrid space search in the mutation. The GA is well-designed to find feasible solutions while considering examining three quantities of waste in collection locations produced from synthetic data. Experimental results show that the proposed approach outperforms other previous research approaches addressing the same constraints and with a similar dataset. It is noteworthy that the proposed approach achieved an average reduction in errors in 100 instances compared to previous research by more than 81%. Whereas for 500 instances, the reduction in error reached more than 59%, while in1000 instances, the error was reduced by more than 44%. Those promising results proved that GA utilization can improve the waste collection process through identifying the best appropriate tours that can minimize the cost via distance minimization, energy conservation and capacity maximization mechanisms. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： Genetic algorithm (GA) multi-objective constraints path planning unmanned aerial vehicles (UAV) waste collection waste management system

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Simulation of high-dimensional collaborative distribution of disaster materials with time window constraints

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期44卷 7543-7552页

作者： Xu, Baohua Chen, Jiayu Li, Zhi Yang, Tao State Grid Anhui Procurement Co Hefei 361024 Anhui Peoples R China Anhui Jiyuan Software Co Ltd Hefei Anhui Peoples R China

In recent years, with the continuous occurrence of natural disasters, people have gradually realized the importance of improving emergency response capability, and the weight of time constraints for rational allocation of emergency materials has gradually increased. Therefore, a high-dimensional collaborative allocation method of disaster materials with time window constraints is studied. A high-dimensional collaborative distribution model of disaster materials with time window constraints is constructed by combining four dimensional decision-making indexes: maximizing the satisfaction of material demand, fairness of material distribution and minimizing the total cost of expected emergency response;Build SPEA2 + SDE hybrid algorithm, solve the model and output the optimal solution set. The simulation results show that this method can have the ability of high-dimensional distribution of disaster materials, obtain the output of the optimal distribution scheme set of disaster materials, and the material satisfaction is more than 0.70. Under the condition of minimum distribution cost, the distribution of disaster materials can be completed.

关键词： Time windowconstraint disaster materials high dimensional collaborative allocation multi-objective constraints decision index

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Swarm Intelligence-Inspired Meta-Heuristics Hybrid Optimization for multi-Constraint Routing in Vehicular Adhoc Networks

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IETE JOURNAL OF RESEARCH 2024年第1期70卷 95-115页

作者： Suganyadevi, K. Nandalal, V. Sri Eshwar Coll Engn Elect & Commun Engn Dept Coimbatore Tamilnadu India Sri Krishna Coll Engn & Technol Dept Elect & Commun Engn Coimbatore Tamilnadu India

In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system's recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO.

关键词： VANET Shortest path multi-objective constraints ACO optimization Traffic prediction Data transmission

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Optimal allocation of material dispatch in emergency events using multi-objective constraint for vehicular networks

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WIRELESS NETWORKS 2022年第8期28卷 3715-3727页

作者： Liu, Chen Qian, Yuhao Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Nanjing Univ Finance & Econ Inst Food & Strateg Reserves Nanjing 210003 Peoples R China

In the early stage of large-scale disasters, the first batch of emergency supplies are often in short supply, and decision-makers responsible for material distributions need to send emergency materials to the recipients in the shortest possible time, while also taking into account the minimum transportation costs. In these scenarios, the traditional particle swarm algorithm has been frequently used, however it faces the challenge of "precocious puberty" and is unable to resolve the scheduling problem. To solve this issue, this paper proposes an optimization model for material dispatch in emergency events using a non-dominant sorting algorithm for vehicular communication. The model first satisfies the shortest delivery time and material demand, establishes the shortest route for vehicle travel, and then proposes a multi-objective uncontrolled solving ant colony algorithm to break through the bottleneck of the juvenile algorithm by solving the problems of convergence of NSGA-II algorithm and uneven distribution of Pareto front surface. Moreover, the objective function and constraints for vehicles at each emergency supply point are defined, which must not exceed the total number of available vehicles. The case study shows that the Pareto non-inferior solution searched by NSGA-II is ideal under the premise that multiple goals are optimal, and the Pareto non-inferior solution scheme available for researchers to choose is improved. The model and algorithm objectively optimize the overall layout of emergency material distribution.

关键词： multi-objective constraints vehicular networks emergency events optimal distribution of materials

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FGMTS: Fractional grey wolf optimizer for multi-objective task scheduling strategy in cloud computing

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期35卷 831-844页

作者： Sreenu, Karnam Malempati, Sreelatha Acharya Nagarjuna Univ ANU Coll Engn Dept Comp Sci & Engn Guntur Andhra Prades India RVR & JC Coll Engn Dept Comp Sci & Engn Guntur Andhra Prades India

Task scheduling on heterogeneous data centers of cloud computing environment is a challenging problem. The efficiency of the cloud depends on the adopted task scheduling strategy. The task scheduling algorithm schedules the required task resources of application in the cloud platform. Even though many algorithms are presented for task scheduling;they consider only the minimum objectives for the trade-off of optimal scheduling. In this paper, a multi-objective task scheduling strategy is proposed for the task scheduling problem in the cloud network as an NP-hard optimization problem. In order to solve the scheduling problem, Fractional Grey wolf multi-objective optimization-based Task Scheduling strategy (FGMTS) is newly proposed for scheduling tasks in the cloud. The proposed FGMTS algorithm is the combination of the existing fractional theory and Grey Wolf Optimizer algorithm. Also, the multi-objective function is newly formulated to solve the multi-objective scheduling problem. The fitness function for the proposed optimization considers the parameters, such as Execution time, Communication time, Execution cost, Communication cost, Energy, and Resource utilization for optimal scheduling. The experimentation of the proposed task scheduling strategy is carried out over two cloud setups. The performance of proposed system is validated over the existing techniques, such as PSO, GA, and GWO using the metrics considered in the multi-objective formulation function. The experimental results show that the proposed FGMTS-Task scheduling scheme allocates the resource for all incoming task requests while preserving the performance of the cloud with an increase in the profit.

关键词： Cloud computing task scheduling fractional theory grey wolf optimization algorithm multi-objective constraints

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multi-objective Constrained Optimizations Based Fuzzy Fault Tolerance Algorithm of Quarter Vehicle Suspensions

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IEEE ACCESS 2022年 10卷 4349-4358页

作者： Wang, Jidong Li, Jifang North China Univ Water Resources & Elect Power Sch Elect Power Zhengzhou 450011 Henan Peoples R China

In this paper, a novel fuzzy fault tolerance algorithm is proposed for quarter vehicle active suspensions based on multi-objective constrained optimizations, which improves the riding comfort and driving safety. The objective function approach the optimal solutions via integrating multi-objective constraints as cost functions. By introducing the barrier function, the nonlinear active suspensions with multi-objective constraints are transformed into pure feedback systems without constraints. The problem is rather complicated yet challenging if the actuator faults are taken into account in quarter vehicle. The actuator faults are solved by the utilization of proportional actuation method. The unknown smooth dynamics based on physical truth are identified by exploiting the fuzzy logic knowledge. Meanwhile, the signals received in the suspensions do not violate the constraint boundary. Finally, the simulation results show that the algorithm is effective.

关键词： Suspensions (mechanical systems) Actuators Automobiles Adaptive systems Vehicle dynamics Safety Fuzzy logic multi-objective constraints active suspensions constrained optimization fault-tolerance algorithm fuzzy logic knowledge

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