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Path planning and trajectroy tracking of a mobile robot using bio-inspired optimization algorithms and PID control 24

Path planning and trajectroy tracking of a mobile robot usin...

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24th IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)

作者： Moshayedi, Ata Jahangir Abbasi, Amin Liao, Liefa Li, Shuai Jiangxi Univ Sci & Technol Sch Informat Engn 86 Hongqi Ave Ganzhou 341000 Jiangxi Peoples R China Azad Univ Dept Elect Engn Khomeinishahr Branch Esfahan Iran Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781538683446

Path planning and trajectory tacking are the fundamental task in mobile robotic science, and they enable the robot to navigate autonomously. In this work, the path planning task is carried out using three bio-inspired optimization algorithms, including PSO, ABC and FA. The duty of the algorithms is to determine a collision-free path through fixed obstacles in the working environment. The maximum speed of the robot is applied to the optimization problem as a constraint. In order to evaluate the performance of the algorithms, four workspaces with different obstacle layout are simulated in MATLAB, and the quality of path planning task is analyzed statistically and numerically, considering four different criteria, including, convergency quality, convergency time, path length and success rate. In the next step, a control model is designed to track the path curve determined by the path planning algorithms. A PID-based control structure is simulated in MAT LAB Simulink and the controller was able to track the pre-determined trajectories with proper approximation. The controller is applied on a dynamic model of a two-wheeled mobile robot offered by [1]. In order to validate the control inputs it is necessary to apply them on a real platform. The experimental study is implemented on a two-wheeled mobile robot which is designed and built based on the authors' previous paper [2] in various enverioment and obstacles. The result shows control inputs were applied to the real robot and the robot was able to imitate the applied path curve, and find its way toward the target point without colliding obstacles in real and simulation task.

关键词： Path Planning Trajectory Tracking Wheeled Mobile Robot bio-inspired algorithms PSO ABC FA

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Performance Analysis of bio-inspired Scheduling algorithms for Cloud Environments 30

Performance Analysis of Bio-Inspired Scheduling Algorithms f...

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30th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Al Buhussain, Ali De Grande, Robson E. Boukerche, Azzedine Univ Ottawa PARADISE Res Lab Ottawa ON Canada

ISBN: (纸本)9781509036820

Cloud computing environments mainly focus on the delivery of resources, platforms, and applications as services to users over the Internet. Cloud promises users access to as many resources as they need, making use of an elastic provisioning of resources. The cloud technology has gained popularity in recent years as the new paradigm in the IT industry. The number of users of Cloud services has been increasing steadily, so the need for efficient task scheduling is crucial for maintaining performance. In this particular case, a scheduler is responsible for assigning tasks to virtual machines efficiently;it is expected to adapt to changes along with defined demand. In this paper, we present a comparative performance study on bio-inspired scheduling algorithms: Ant Colony Optimization (ACO) and Honey Bee Optimization (HBO). A networking scheduling algorithm, Random Biased Sampling, is also evaluated. Those algorithms show the ability of self-managing and adapting to changes in the environment. The experimental results have shown that ACO performs better when computation power is set as the objective, and HBO shows better scheduling when the objective mainly relies on costs.

关键词： Scheduling bio-inspired algorithms Swarm Optimization

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Scheduling System for Multiple Self-driving Cars Using K-Means and bio-inspired Optimization algorithms

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SN Computer Science 2023年第5期4卷 647页

作者： Silva, Clênio E. César, Tiago S. Gomes, Iago P. Silva, Júnior A. R. Wolf, Denis F. Alves, Raulcézar Souza, Jefferson R. Faculty of Computing Federal University of Uberlândia Minas Gerais Uberlândia Brazil Institute of Mathematical and Computer Sciences University of São Paulo São Paulo São Carlos Brazil

This paper presents the development of a hybrid approach as a solution to the multiple Traveling Salesman Problem (mTSP) applied to the route scheduling for self-drive cars. First, we use k-means to generate routes that equality distribute delivery locations among the cars. Then, these routes are set as the initial population for bio-inspired algorithms, such as Genetic Algorithm (GA) and Ant Colony System (ACS), which perform an evolutionary process to find a route which minimizes the overall distance while keeping the balance of individual tours of each car. The experiments were conducted with our route scheduling system in real and virtual environments. We compared our hybrid approaches using k-means in conjunction with GA and ACS against GA, ACS, and Particle Swarm Optimization (PSO) initialized with random population. The results showed that, as the number of cars and target locations increase, the hybrid modeling approaches outperform GA, ACS, and PSO without any pre-processing. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： bio-inspired algorithms Multiple Traveling Salesman Problem (mTSP) Route scheduling system Self-driving cars

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A comparative analysis of bio-inspired optimization algorithms for automated test pattern generation in sequential circuits

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APPLIED SOFT COMPUTING 2021年 101卷 106967-106967页

作者： Alateeq, Majed Pedrycz, Witold Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2VR Canada King Khalid Univ Comp Engn Dept Abha Asir Saudi Arabia

In recent years, bio-inspired optimization algorithms have demonstrated the ability to produce optimal solutions to numerous complex computational problems in science and engineering. In this work, a comparative analysis of bio-inspired algorithms is presented to understand and quantify the performance of algorithms in guiding the search process toward better solutions over all feasible solutions. Three evolutionary algorithms, namely, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), are implemented to generate an optimized test sequence set for digital sequential circuits to investigate how they explore and the exploitation realized in their search spaces. The merit of using a comparative method to analyze and optimize search spaces is to reach a reliable and quantifiable conclusion about the relative performance of each algorithm. An improvement in the quality of solutions was achieved, particularly in terms of testing time, number of test vectors, and fault coverage of tested sequential circuits in comparison with other algorithmic test generators that have been presented in the literature. The study shows how to effectively reduce the search space without negatively affecting the results and guides the search process over a large space. In addition, the experiments highlight the limitations of each optimization algorithm and offer some constructive methods of improvement. Moreover, several recommendations and guidelines regarding the use of optimization algorithms as test pattern generators to improve their performance and increase their efficiency are presented. Finally, we emphasize the relevance of bioinspired algorithms in solving complex computational problems, data manipulation, and optimization objectives. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Genetic Algorithm Particle Swarm Optimization Differential Evolution Test pattern generation bio-inspired algorithms Comparative analysis

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Design of a resilient wide-area damping controller using time delays

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ELECTRICAL ENGINEERING 2025年第1期107卷 1111-1124页

作者： Bento, Murilo E. C. Univ Fed Rio de Janeiro BR-21941909 Rio De Janeiro Brazil

The presence of oscillation modes in a power system with low damping rates can compromise its proper operation and motivated the development of different damping control projects to improve these damping rates. Central damping controllers using PMU data showed to be able to mitigate oscillation modes of power systems with high damping ratios. However, time delays and vulnerability of communication channels to cyber-attacks can damage the desired operation of this type of damping controller. This paper proposes an optimization problem-based method for designing centralized controllers that are resilient to channel losses and using time delays as variables of the proposed optimization procedure. The bio-inspired algorithms Marine Predators Algorithm, Particle Swarm Optimization and Genetic algorithms were applied and evaluated in the proposed optimization model. The IEEE 68-bus was used as a test system, and a set of case studies were carried out. The obtained results show that considering time delays as optimization variables can be beneficial to achieve optimal control objectives. Furthermore, the communication failure robustness strategy was effective as observed by the modal analyses and nonlinear time domain simulations of the test system under contingencies.

关键词： Smart grids Power system stability bio-inspired algorithms Wide-area damping controller Low-frequency oscillation modes

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A novel arctic fox survival strategy inspired optimization algorithm

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2025年第1期49卷 1-73页

作者： Subha, E. Jothi Prakash, V. Antran Vijay, S. Arul Karpagam Coll Engn Coimbatore 641032 Tamil Nadu India

In the field of optimization algorithms, nature-inspired techniques have garnered attention for their adaptability and problem-solving prowess. This research introduces the Arctic Fox Algorithm (AFA), an innovative optimization technique inspired by the adaptive survival strategies of the Arctic fox, designed to excel in dynamic and complex optimization landscapes. Incorporating gradient flow dynamics, stochastic differential equations, and probability distributions, AFA is equipped to adjust its search strategies dynamically, enhancing both exploration and exploitation capabilities. Through rigorous evaluation on a set of 25 benchmark functions, AFA consistently outperformed established algorithms particularly in scenarios involving high-dimensional and multi-modal problems, demonstrating faster convergence and improved solution quality. Application of AFA to real-world problems, including wind farm layout optimization and financial portfolio optimization, highlighted its ability to increase energy outputs by up to 15% and improve portfolio Sharpe ratios by 20% compared to conventional methods. These results showcase AFA's potential as a robust tool for complex optimization tasks, paving the way for future research focused on refining its adaptive mechanisms and exploring broader applications.

关键词： Arctic fox algorithm Optimization techniques Dynamic environments bio-inspired algorithms Multi modal optimization

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AI-driven optimization of indoor environmental quality and energy consumption in smart buildings: a bio-inspired algorithmic approach

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2025年

作者： Ghoneim, Rehab Salaheldin Arabasy, Mazin Abdulhadi, Ashraf Nadheer Al Ahliyya Amman Univ Fac Architecture & Design Dept Interior Design Amman 19111 Jordan Gulf Univ Coll Engn Dept Architecture & Interior Design Sakhir Bahrain

This work presents an in-depth analysis of five bio-inspired optimization algorithms, namely Puma Optimizer (PO), Walrus Optimizer (WO), Flying Fox Optimization Algorithm (FFO), Waterwheel Plant Algorithm (WWPA), and Energy Valley Optimizer (EVO), which optimize the energy consumption while keeping the occupants' comfort intact within smart building environments. It emulates operational challenges such algorithms might face in the real world for testing, such as dynamic energy demand, fluctuating occupancy, and time-varying weather, to meet the perfect balance between energy efficiency and indoor environmental quality. Key findings indicate that the algorithms achieve significant energy savings and maintain stable temperature and humidity levels across different zones. The comparison provides insight into each algorithm's strengths in various scenarios and, potentially, in real-time smart building management systems applications. Further, integrations of multidimensional visualization techniques enhance the trade-off interpretations between energy consumption and occupants' comfort. Thus, it is a valuable reference for sustainable building design.

关键词： bio-inspired algorithms smart buildings energy optimization occupant comfort machine learning

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Comparative analysis of accuracy and computational complexity across 21 swarm intelligence algorithms

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EVOLUTIONARY INTELLIGENCE 2025年第1期18卷 1-37页

作者： Warnakulasooriya, Kolitha Segev, Aviv Univ S Alabama Sch Comp Mobile AL 36688 USA

Nonlinear, complex optimization problems are prevalent in many scientific and engineering fields. Traditional algorithms often struggle with these problems due to their high dimensionality and intricate nature, making them time-consuming. Many researchers have proposed new metaheuristic algorithms inspired by biological behaviors in nature, which comparatively show higher performance and accuracy than traditional optimization algorithms. Nature-inspired algorithms, particularly those based on swarm intelligence, offer adaptable and efficient solutions to these challenges. In recent years, swarm intelligence algorithms have made significant advancements. Classical and CEC benchmark suits are immersively useful for studying the performance of optimization algorithms. According to our literature survey, we identified that many algorithms were evaluated based on accuracy. Currently, swarm intelligence algorithms are used in many applications, and efficiency and computational complexity need to be evaluated. A broad-level study of the computational complexity and accuracy of popular swarm intelligence algorithms has not been done recently. Therefore this study we comprehensively evaluate and compare 21 bio-inspired swarm intelligence algorithms on eight non-separable unimodal, eight separable unimodal, five non-separable multimodal, seven separable multimodal functions, and two CEC 2018 many objective functions. We study the structure and mathematical model of the selected algorithms. Then we categorized selected algorithms into six different behavioral groups. We calculated the root mean square error between expected and actual values. Then we performed an RMSE cross-validation statistical test to understand how accurately an algorithm resolves an average problem. We found that Artificial Lizard Search Optimization (ALSO) is the most prominent algorithm in accuracy and efficiency. Besides that, Cat Swarm Optimization (CSO), Squirrel Search Algorithm (SSA), and Chimp Opt

关键词： Swarm intelligence bio-inspired algorithms Evolutionary algorithms Performance evaluation

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biologically-inspired algorithms for Adaptive Non-Player Character Behavior in Video Games 7th

Biologically-Inspired Algorithms for Adaptive Non-Player Cha...

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7th International Conference on Optimization and Learning

作者： Alizadeh, Sina Mouhoub, Malek Univ Regina Dept Comp Sci Regina SK Canada

ISBN: (纸本)9783031779404;9783031779411

Video game designers frequently employ different movement algorithms to give players the impression of the intelligent and unpredictable motions of non-player characters or enemy agents. This paper explores the application of metaheuristic biological algorithms in video games to enhance the unpredictability and realism of enemy characters, moving away from traditional pre-programmed NPC behaviors. Developing the bespoke game "Run and Catch" incorporates the utilization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for directing the movement of non-player characters. We tackle the premature convergence challenge by introducing an adaptive Finder - Tracker agent approach. This mechanism maintains diversity in enemy movements to ensure responsiveness to real-time player character activities. The findings illustrate the adverse effects of early convergence on both immersion and the variety of non-player character movements within a 2D game environment. Moreover, the study underscores the success of ACO and PSO, equipped with the Finder- Tracker agent framework, in reshaping group movements of non-player characters and effectively dealing with the convergence challenge.

关键词： bio-inspired algorithms Video Games Non-Player Characters (NPCs)

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Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence algorithms 5th

Last-Mile Delivery Optimization Using Mixed Electric Vehicle...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Hanaa, Bouhia Benhra, Jamal Ed-dehbi, Wissal ESITH Casablanca Morocco Univ Hassan 2 ENSEM Casablanca Morocco

ISBN: (纸本)9783031807749;9783031807756

This paper presents the Electric Vehicle Routing Problem with Drones (EVRPD), a novel strategy aimed at enhancing last-mile delivery through the combined use of Electric Vehicles (EVs) and drones. The primary objective is to reduce energy usage and CO2 emissions while maintaining effective delivery operations. The proposed system employs a diverse fleet where EVs act as mobile bases, and drones handle the final leg of parcel delivery. Both EVs and drones can deliver packages, with drones being utilized for smaller, time-sensitive shipments. The system emphasizes optimal payload distribution to boost energy efficiency and drone endurance. Drones are resupplied on the EVs' rooftops via IoT technology. To address the routing challenge, the solution leverages bio-inspired algorithms, including Ant Colony Optimization (ACO) for EVs and Particle Swarm Optimization (PSO) for drones. This methodology enhances energy efficiency and drone autonomy, ensuring effective parcel delivery. By incorporating these advanced techniques, EVRPD presents a promising approach for a more sustainable and effective urban logistics network.

关键词： EVRPD Last-mile delivery Energy efficiency CO2 reduction EVs Drones bio-inspired algorithms ACO PSO

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