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2024 International Conference on Intelligent Robots and Systems

作者： Warke, William D. Ramos, J. Humberto Ganesh, Prashant Brink, Kevin M. Hale, Matthew T. Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA EpiSci Sci Inc Poway CA USA Air Force Res Lab Munit Directorate Eglin Afb Eglin AFB FL USA Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9798350377712;9798350377705

It is common in pose graph optimization (PGO) algorithms to assume that noise in the translations and rotations of relative pose measurements is uncorrelated. However, existing work shows that in practice these measurements can be highly correlated, which leads to degradation in the accuracy of PGO solutions that rely on this assumption. Therefore, in this paper we develop a novel algorithm derived from a realistic, correlated model of relative pose uncertainty, and we quantify the resulting improvement in the accuracy of the solutions we obtain relative to state-of-the-art PGO algorithms. Our approach utilizes Riemannian optimization on the planar unit dual quaternion (PUDQ) manifold, and we prove that it converges to first-order stationary points of a Lie-theoretic maximum likelihood objective. Then we show experimentally that, compared to state-of-the-art PGO algorithms, this algorithm produces estimation errors that are lower by 10% to 25% across several orders of magnitude of correlated noise levels and graph sizes.

关键词： optimization algorithms

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A Chaotic Elite Cloning Artificial Jellyfish Algorithm for Efficient Task Allocation in IOTWSNs

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IEEE SENSORS JOURNAL 2025年第4期25卷 6905-6919页

作者： Wen, Dikun Cao, Qike Feng, Shourui Zhang, Zhehao Zhou, Peng Henan Univ Sch Software Kaifeng 475000 Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Peoples R China Sun Yat Sen Univ Sch Life Sci Guangzhou 510275 Peoples R China Henan Univ Econ & Law Sch Stat & Big Data Zhengzhou 450000 Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Urumqi 841000 Xinjiang Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China

The Internet of Things wireless sensor networks (IOTWSNs) are crucial in modern smart systems, where self-organizing sensor nodes enable efficient and flexible network structures for applications like environmental monitoring and smart cities. The task allocation problem in IOTWSNs is NP-hard, making effective strategies essential for optimal network performance. This article proposes an improved artificial jellyfish search algorithm (CECJS) that integrates chaotic initialization, elite, and cloning strategies to enhance global search ability and convergence speed. To evaluate CECJS's efficiency, the article introduces network gain, reflecting both network effectiveness and task completion quality. Experimental results show that CECJS significantly outperforms traditional algorithms like genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO) in task allocation gains, achieving improvements of several to tens of percentage points. In addition, CECJS exhibits faster convergence, finding near-optimal solutions more efficiently, making it an effective solution for large-scale IOTWSNs task optimization.

关键词： Resource management Sensors Intelligent sensors Cloning optimization Heuristic algorithms Wireless sensor networks Jellyfish Convergence Medical services IoT wireless sensor networks jellyfish search algorithm optimization algorithms performance evaluation task allocation

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A seasonal grey model for forecasting energy imports demand from information differences perspective

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APPLIED MATHEMATICAL MODELLING 2025年 140卷

作者： Zhou, Weijie Chang, Jiaxin Zuo, Weizhen Wang, Feifei Changzhou Univ Sch Wujinglian Econ Changzhou 213159 Peoples R China Changzhou Univ Business Coll Changzhou 213159 Peoples R China

This paper focuses on the critical task of accurately predicting seasonal energy import demand for countries heavily reliant on foreign energy supplies. Given the significance of optimizing energy supply structures and formulating effective policies, precise forecasting is imperative. However, due to complex cyclical fluctuations, it is challenging to construct a prediction model that can handle seasonal sequences with multiple patterns and trends. And the construction of the model relies on the extraction of data information. Based on the differences impacts between new and old data information on future predictions, this paper proposes a new seasonal grey power model using new-information-priority principle. Nine types of seasonal series with different cycles, trends and patterns, and lengths are used to test the adaptability of the new model. Among these, the seasonal data distributions, and modelling effect of daily and hourly series are studied for the first time in grey forecasting system. Furthermore, three monthly energy imports demand, including natural gas, liquefied petroleum gas, crude oil imports are utilized to verify fitting and predicting accuracy of the proposed model. The results demonstrate that the new model exhibits higher prediction accuracy and lower volatility compared to other models, containing prevailing grey models, machine learning models, and statistical models. Besides, the performance of different swarm intelligent algorithms, such as Cultural Algorithm, Genetic Algorithm, Particle Swarm optimization, and Simulated Annealing optimization algorithms in hyper-parameter optimization of the new model is analyzed. Finally, the new model generates forecasts for three energy imports demands each month from 2023 to 2026. And some practical implications and policy suggestions are also discussed.

关键词： Data trends and patterns Energy imports demand New-information-priority Seasonal grey power model Application scope analysis optimization algorithms

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Optimal Impact Angle Guidance via First-Order optimization Under Nonconvex Constraints

Optimal Impact Angle Guidance via First-Order Optimization U...

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American Control Conference (ACC)

作者： Park, Gyubin Choi, Jiwoo Jeong, Da Hoon Kim, Jong-Han Inha Univ Dept Aerosp Engn Incheon South Korea Hyundai Motors Co Vehicle Control Dev Ctr Seoul South Korea

ISBN: (纸本)9798350382662;9798350382655

Most of the optimal guidance problems can be formulated as nonconvex optimization problems, which can be solved indirectly by relaxation, convexification, or linearization. Although these methods are guaranteed to converge to the global optimum of the modified problems, the obtained solution may not guarantee global optimality or even the feasibility of the original nonconvex problems. In this paper, we propose a computational optimal guidance approach that directly handles the nonconvex constraints encountered in formulating the guidance problems. The proposed computational guidance approach alternately solves the least squares problems and projects the solution onto nonconvex feasible sets, which rapidly converges to feasible suboptimal solutions or sometimes to the globally optimal solutions. The proposed algorithm is verified via a series of numerical simulations on impact angle guidance problems under state dependent maneuver vector constraints, and it is demonstrated that the proposed algorithm provides superior guidance performance than conventional techniques.

关键词： optimization algorithms

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Distributed optimization-Based State Estimation of Nonlinear Dynamical Systems

Distributed Optimization-Based State Estimation of Nonlinear...

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American Control Conference (ACC)

作者： Wang, Lili Sundaram, Shreyas LeGrand, Keith Purdue Univ Sch Aeronaut & Astronaut W Lafayette IN 47907 USA Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN USA

ISBN: (纸本)9798350382662;9798350382655

We consider the problem of enabling a network of agents to estimate the state of a discrete-time nonlinear dynamical system. At each time step, each agent in the network receives a measurement characterized by a nonlinear function of the system state and exchanges information with its neighbors in the network. We propose an optimization-based estimator where agents collaboratively solve a distributed optimization problem while satisfying a communication constraint in the form of a fixed number of distributed optimization iterations at each estimation time step. Subject to the assumptions that the system is collectively observable, and the communication network is time-varying and strongly connected, we show that for any given lambda which satisfies 0 < lambda < 1, it is possible to choose q, the number of the distributed optimization iterations, so that the estimation error for each agent converges to zero at least as fast as lambda(t) does.

关键词： optimization algorithms

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Competitive Online Age-of-Information optimization for Energy Harvesting Systems

Competitive Online Age-of-Information Optimization for Energ...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Lin, Qiulin Su, Junyan Chen, Minghua City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

ISBN: (纸本)9798350383515;9798350383508

We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm CR-Reserve and show it achieves (ln theta + 1)-competitive, where. is a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 12% performance improvement as compared to the state-of-the-art baseline.

关键词： optimization algorithms

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Flight tracking control of mini-drone: Flower pollination algorithm based model-free control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第6期239卷 578-592页

作者： Glida, Hossam Eddine Abdelghani, Chelihi Chouki, Sentouh Univ Caen Normandie LIS UR 7478 UNICAEN ENSICAEN F-14000 Caen France Univ Biskra Dept Elect Engn Lab LI3CUB Biskra Algeria Constantine 1 Univ Fac Technol Dept Elect Constantine Algeria Univ Polytech Hauts Defrance UMR 8201 CNRS LAMIH Valenciennes France INSA Hauts Defrance Valenciennes France

This paper presents a novel model-free control approach, Flower Pollination Algorithm-based Model-Free Control (FPA-MFC), for trajectory tracking of mini-drone quadrotor unmanned aerial vehicles (UAVs). The proposed approach employs an adaptive estimator based on filtered signals to approximate the nonlinear dynamic functions of the system. This approximator allows the development of a robust decentralized control law able to separately manage the position and attitude dynamics of the drone. The controller design is free of any prior knowledge of the system dynamics, and the control inputs are computed solely from instantaneous input and output measurements. Indeed, this can significantly reduce the computational burden and improve the efficiency of the control algorithm while preserving its simplicity. The design gains of the control law are selected using the metaheuristic flower pollination algorithm to achieve greater trajectory tracking performance and ensure closed-loop system stability. Simulation tests conducted on the Parrot mini drone platform validate the effectiveness and superior performance of FPA-MFC, compared to similar controllers without optimization and using the particle swarm optimization algorithm.

关键词： Mini-drone model-free control optimization algorithms flower pollination algorithm

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Multi-objective and blockchain based optimization algorithm for fleet sharing management

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OPSEARCH 2024年第3期61卷 1131-1153页

作者： Bhardwaj, Rashmi Garg, Shanky Guru Gobind Singh Indraprastha Univ GGSIPU Univ Sch Basic & Appl Sci USBAS Delhi 110078 India

With the rise in the demand for the fleet in today's time even for covering a small distance, taxis play a crucial role not only in terms of ease in traveling but also in generating profit economically. Every advantage comes up with the side effects behind it such as the increase in profit along with the comfort of the customers have effects on the environment and also have issues related to the trust. Use of these services in excess sometimes poses a major threat to the environment in terms of doubling the traffic volume as the personal vehicles along with the hired vehicle volume are added together. It will also increase pollution which will affect our health. So, there is a need to devise a way that minimizes the negative effects of these services along with the increase in economic performance. This paper deals with the sharing ability of these platforms so that optimal allocation of passengers will be done to minimize the side effects and also to increase the overall usage of the fleet. However, the sharing ability of these services comes up with the disadvantage of having a trust issue among the users. So, to deal with this problem, we incorporate the blockchain concept here. This study includes the multi-objective functions such as the shortest route, maximal allocation of the passenger along with the minimization of cost which in turn reduces its effect on the environment, and minimization of trust-related problems with respect to the constraints such as demand, time, and supply. This study proposes two different optimization algorithms to solve this problem. The first one is based on considering one objective in each stage at a time where the objectives are dependent on each other whereas the second one is the formulation of a Multi-objective optimization algorithm by taking all the objectives from all the stages together with their respective weights by keeping in mind the problems related to these services. A case study is done based on this problem and s

关键词： Multi-objective Fleet sharing Blockchain Pollution optimization algorithms

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An Orchestrated Survey on T-Way Test Case Generation Strategies Based on optimization algorithms

An Orchestrated Survey on <i>T</i>-Way Test Case Generation ...

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8th International Conference on Robotic, Vision, Signal Processing and Power Applications (RoViSP)

作者： Al-Sewari, AbdulRahman A. Zamli, Kamal Z. Univ Malaysia Pahang Software Engn Dept Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia

ISBN: (纸本)9789814585422;9789814585415

The test case construction is amongst the most labor-intensive tasks and has significant influence on the effectiveness and efficiency in software testing. Due to the market needed for diverse types of tests, recently, several number of t-way testing strategies (where t indicates the interaction strengths) have been developed adopting different approaches Algebraic, Pure computational, and optimization algorithms (OpA). This paper presents an orchestrated survey of the existing OpA t-way strategies as Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Algorithm (ACA), Particle Swarm optimization based strategy (PSTG), and Harmony Search Strategy (HSS). The results demonstrate the strength and the limitations of each strategy, thereby highlighting possible research for future work in this area.

关键词： T-way testing Test case generation Software and hardware testing optimization algorithms

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A UAV Single Station Passive Location Algorithm Based on Harris Trevally Fish optimization 16

A UAV Single Station Passive Location Algorithm Based on Har...

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16th International Conference on Microwave and Millimeter Wave Technology (ICMMT)

作者： Gao, Jingpeng Zhao, Pengjie Zhang, Tianran Qian, Jianfei Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Beijing Inst Elect Syst Engn Beijing 100854 Peoples R China

ISBN: (纸本)9798350389968

The traditional swarm intelligence optimization algorithm is used to solve the UAV single station passive location with reduced positioning accuracy due to the continuous motion of the UAV. To address this problem, this paper proposes a UAV single station passive location algorithm based on Harris Trevally Fish optimization (HTFO). The algorithm combines historical and real-time phase difference information, and introduces the Harris factor and Host foraging ideology. It improves the algorithm's searching ability while avoiding the algorithm from falling into local optimum. The simulation results show that the proposed algorithm is better than the traditional algorithm in performance stability, and the positioning accuracy is improved by 6.13%, which has good value for engineering applications.

关键词： optimization algorithms

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