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VSC-HVDC system optimized PI controllers using bacterial foraging algorithm

ELECTRICAL ENGINEERING 2015年第3期97卷 205-212页

作者： Mazouz, Lakhdar Zidi, Sid Ahmed Saadi, Slami Khatir, Mohamed Benmessaoud, Tahar Sidi Belabbes Univ ICEPS Lab Sidi Bel Abbes Algeria Djelfa Univ Fac Sci & Technol Djelfa Algeria

The strategy of control for high-voltage direct current system on voltage-source converter VSC-HVDC is based on an important element which is PI controller. Because of its simple structure and strong robustness in a wide range of serving conditions, it has been used in the control system in the last few years. Therefore, it is important for the VSC-HVDC system permanent regime to choose proper PI parameters. The use of conventional techniques to find suitable PI parameters creates a number of challenges for the system operators, because it is a difficult process and time consuming. In this paper, a new swarm intelligence algorithm called bacterial foraging optimization is introduced to find the optimal parameters of the PI controller. This approach offers great flexibility for the permanent regime recovery and improves the stability of the VSC-HVDC link compared to conventional techniques. The obtained results are presented to show the effectiveness of the proposed swarm implementation for optimal controller design for VSC-HVDC transmission. MATLAB/Similink simulations are provided to demonstrate the performance of the proposed approach.

关键词： VSC-HVDC BFO approach Optimization PI controller swarm algorithms Parameters Performance

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Robot algorithms for Localization of Multiple Emission Sources

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ACM COMPUTING SURVEYS 2011年第3期43卷 1–25页

作者： Mcgill, Kathleen Taylor, Stephen Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

The problem of time-varying, multisource localization using robotic swarms has received relatively little attention when compared to single-source localization. It involves distinct challenges regarding how to partition the robots during search to ensure that all sources are located in minimal time, how to avoid obstacles and other robots, and how to proceed after each source is found. Unfortunately, no common set of validation problems and reference algorithms has evolved, and there are no general theoretical foundations that guarantee progress, convergence, and termination. This article surveys the current multisource literature from the viewpoint of these central questions.

关键词： algorithms Performance Reliability Standardization Source localization mobile robotic networks swarm algorithms hill-climbing algorithms biologically inspired algorithms Bayesian occupancy mapping Bayesian filters

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A new algorithm inspired in the behavior of the social-spider for constrained optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第2期41卷 412-425页

作者： Cuevas, Erik Cienfuegos, Miguel Univ Guadalajara Dept Elect CUCEI Guadalajara 44430 Jalisco Mexico

During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one;(2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： swarm algorithms Constrained optimization Bio-inspired algorithms

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Simultaneous feature selection and SVM optimization based on fuzzy signature and chaos GOA

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EVOLVING SYSTEMS 2024年第5期15卷 1907-1937页

作者： Zandvakili, Aboozar Javidi, Mohammad Masoud Mansouri, Najme Shahid Bahonar Univ Kerman Dept Comp Sci Kerman Iran Bahonar Univ Kerman Fac Shahid Kerman Iran

In machine learning models, feature selection plays a crucial role. It reduces overall data, minimizes storage requirements, and enhances algorithm performance. Despite this, greedy and exhaustive search methods may not be optimal as the number of features increases. Metaheuristic algorithms are a more sensible way to deal with this issue. In this study, the Grasshopper Optimization Algorithm (GOA) is applied to feature selection. Although GOA may be relatively easy to implement, it may not fully leverage each iteration and may become stuck in local optima. The comfort zone in GOA influences the grasshopper movement within the search space, influencing exploration and exploitation. As a constant, the algorithm changes the comfort zone linearly. The proposed algorithm, Signature Chaos GOA (SCGOA), overcomes these limitations in several ways. Firstly, it constructs the initial population using correlations. Second, unlike existing methods, it specifies specific procedures for initial and final iterations. After the initial iteration, the algorithm adjusts the comfort zone parameters dynamically using chaos theory and fuzzy signatures. Lastly, SCGOA aims to optimize both Support Vector Machine (SVM) parameters and feature subsets simultaneously. Objective functions include classification error, the proportion of selected features, and redundancy. In addition, different algorithms such as the Firefly Algorithm (FA), the Bat Algorithm (BA), and the Particle swarm Optimization (PSO) are compared. In comparison with FA, BA, PSO, and GOA, the proposed algorithm can improve the objective function by 30.6%, 34.9%, 7.6%, and 33.3%, respectively.

关键词： Grasshopper optimization Feature selection Fuzzy signature swarm algorithms

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swarm optimization of stiffeners locations in 2-D structures

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2012年第2期60卷 241-246页

作者： Szczepanik, M. Burczynski, T. Silesian Tech Univ Dept Strength Mat & Computat Mech PL-44100 Gliwice Poland Cracow Univ Technol Inst Comp Sci Computat Intelligence Dept PL-31155 Krakow Poland

The paper is devoted to the application of the swarm methods and the finite element method to optimization of the stiffeners location in the 2-D structures (plane stress, bending plates and shells). The structures are optimized for the stress and displacement criteria. The numerical examples demonstrate that the method based on the swarm computation is an effective technique for solving the computer aided optimal design. The additional comparisons of the effectiveness of the particle swarm optimizer (PSO) and evolutionary algorithms (EA) are presented.

关键词： swarm algorithms optimization finite element method bars plane stress bending plates shells

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A Review on 2-D Supersonic Aerofoil Shape Optimization

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2024年第11期64卷 2559-2570页

作者： Vijayaparkavan, P. Lewise, K. Anton Savio Raj, R. Ajith Karunya Inst Technol & Sci Div Aerosp Engn Coimbatore 641114 Tamilnadu India

Review on various methods of optimization of the aerofoil for the supersonic aircraft is the main subject of this paper. The standard forms of the optimization problem are presented and various techniques for solving the resulting problem depends on the specific application are discussed. A great many optimization techniques exist and it is not possible to provide a complete review in the limited space available here. Rather, an attempt is made to focus on methods that are frequently applied in applications of engineering optimization. The benefits and drawbacks of the various approaches are outlined, and recommendations are provided to help the designer choose the best approach for the specific problem at hand. Where possible, a short figure of a respective optimized aerofoil is presented to aid the discussion of that particular optimization technique. The fundamental theory of supersonic flow is provided.

关键词： supersonic airfoils computational fluid dynamics optimization aerodynamic shape efficiency swarm algorithms

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A REVIEW ON THE SIMULATION OF SOCIAL NETWORKS INSIDE HEURISTIC algorithms 32

A REVIEW ON THE SIMULATION OF SOCIAL NETWORKS INSIDE HEURIST...

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32nd European Conference on Modelling and Simulation (ECMS)

作者： Senkerik, Roman Pluhacek, Michal Viktorin, Adam Kadavy, Tomas Janostik, Jakub Oplatkova, Zuzana Kominkova Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9780993244070

This paper represents a comprehensive review of selected methods for visualization of the population dynamics of the swarm and evolutionary algorithms in the form of networks. The whole idea is based on the obvious similarity between interactions between individuals in a swarm and evolutionary algorithms and for example, users of social networks, society, etc. The analogy between individuals from the population used in an arbitrary evolutionary or swarm-based algorithm and vertices (nodes) of a network is discussed here, as well as between edges in a network and communication between individuals in a population. Simple experiments with four well-known heuristic algorithms are described here, giving an insight into different approaches to the building of the network during metaheuristic run.

关键词： Social networks Graphs Analysis Evolutionary algorithms swarm algorithms

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Energy Optimization for Task Scheduling in Distributed Systems by an Artificial Bee Colony Approach 6

Energy Optimization for Task Scheduling in Distributed Syste...

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6th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Arsuaga-Rios, Maria Vega-Rodriguez, Miguel A. CERN European Org Nucl Res IT Dept CH-1211 Geneva Switzerland Univ Extremadura Dept Technol Comp & Commun Escuela Politecn ARCO Res Grp Caceres Spain

ISBN: (纸本)9781479959372

Green Computing also known as Green IT is becoming a hot topic in the computational field during these last years. Green Computing consists of enabling organizations to make a more rational and efficient use of their technological resources and reduce costs while adopting technologies and working methods more respectful of the environment. Execution time and energy consumption are also conflicting objectives, because faster resources frequently imply higher energy consumptions. In this paper, we optimize both: execution time and energy consumption to resolve the task scheduling problem in Grid environments. MOABC is a swarm algorithm inspired in the bees behaviour and it is compared with MO-FA which is other swarm algorithm inspired in the fireflies behaviour. These algorithms are also compared with the well-known NSGA-II to evaluate their multiobjective properties. Moreover, the best algorithm, MOABC, is compared with MOHEFT, the most popular algorithm for workflow scheduling and with two real grid schedulers as WMS or DBC. The results obtained point out MOABC as the best approach in all the cases studied.

关键词： task scheduling energy optimization multiobjective optimization distributed systems swarm algorithms

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Robust and Scalable swarming of Quadruped Robots for Chemical, Biological, Radiological, and Nuclear Source Localisation 10

Robust and Scalable Swarming of Quadruped Robots for Chemica...

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10th International Conference on Mechatronics and Robotics Engineering (ICMRE)

作者： Islam, Rafiqul Anavatti, Sreenatha Kasmarik, Kathryn Garratt, Matt UNSW Canberra SET Canberra Australia

ISBN: (纸本)9798350394283;9798350394276

In this paper, a robust and scalable method for the localisation of Chemical, Biological, Radiological, and Nuclear (CBRN) sources is introduced using a swarm of quadruped robots. A cornerstone of this work is the development of an advanced simulation environment built on the Robot Operating System 2 (ROS2) and compatible with Gazebo. This environment features a sophisticated physics engine and supports a wide array of sensors, offering unprecedented scalability for a swarm of quadruped robot simulations. Following this, we present a novel swarming algorithm designed to exploit quadruped robots' unique kinematics. This algorithm is an enhanced extension of traditional Particle swarm Optimisation (PSO) and incorporates features such as obstacle avoidance, separation policies, velocity clamping and boundary constraints to optimise the CBRN source localisation process. To rigorously assess the robustness and efficacy of the proposed method, extensive experiments are conducted in three different environmental setups. The results reveal a remarkable convergence and success rate in CBRN source localisation, thereby substantiating the effectiveness of the proposed method.

关键词： CBRN Source Localisation swarm algorithms Autonomous Quadruped Navigation Multi-Robot Coordination

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Performance of the Bison Algorithm on Benchmark IEEE CEC 2017 7th

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7th Computer Science On-Line Conference (CSOC)

作者： Kazikova, Anezka Pluhacek, Michal Senkerik, Roman Tomas Bata Univ Zlin Fac Appl Informat TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9783319911892

This paper studies the performance of a newly developed optimization algorithm inspired by the behavior of bison herds: the Bison Algorithm. The algorithm divides its population into two groups. The exploiting group simulates the swarming behavior of bison herds endangered by predators. The exploring group systematically runs through the search space in order to avoid local optima. At the beginning of the paper, the Bison Algorithm is introduced. Then the performance of the algorithm is compared to the Particle swarm Optimization and the Cuckoo Search on the set of 30 benchmark functions of IEEE CEC 2017. Finally, the outcome of the experiments is discussed.

关键词： Bison Algorithm Bison Optimization swarm algorithms Metaheuristic

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