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Journal articles on the topic 'Swarm based design'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

von Mammen, Sebastian, Scott Novakowski, Gerald Hushlak, and Christian Jacob. "Evolutionary Swarm Design: How Can Swarm-based Systems Help to Generate and Evaluate Designs?" Design Principles and Practices: An International Journal—Annual Review 3, no. 3 (2009): 371–86. http://dx.doi.org/10.18848/1833-1874/cgp/v03i03/37691.

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Mukhlish, Faqihza, John Page, and Michael Bain. "Evolutionary-learning framework: improving automatic swarm robotics design." International Journal of Intelligent Unmanned Systems 6, no. 4 (October 8, 2018): 197–215. http://dx.doi.org/10.1108/ijius-06-2018-0016.

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PurposeThe purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.Design/methodology/approachFirst, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.FindingsThe discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.Originality/valueThis paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.

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Yao, Wenting, and Yongjun Ding. "Smart City Landscape Design Based on Improved Particle Swarm Optimization Algorithm." Complexity 2020 (December 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/6693411.

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Aiming at the shortcomings of standard particle swarm optimization (PSO) algorithms that easily fall into local optimum, this paper proposes an optimization algorithm (LTQPSO) that improves quantum behavioral particle swarms. Aiming at the problem of premature convergence of the particle swarm algorithm, the evolution speed of individual particles and the population dispersion are used to dynamically adjust the inertia weights to make them adaptive and controllable, thereby avoiding premature convergence. At the same time, the natural selection method is introduced into the traditional position update formula to maintain the diversity of the population, strengthen the global search ability of the LTQPSO algorithm, and accelerate the convergence speed of the algorithm. The improved LTQPSO algorithm is applied to landscape trail path planning, and the research results prove the effectiveness and feasibility of the algorithm.

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YEN, GARY G., and MOAYED DANESHYARI. "DIVERSITY-BASED INFORMATION EXCHANGE AMONG MULTIPLE SWARMS IN PARTICLE SWARM OPTIMIZATION." International Journal of Computational Intelligence and Applications 07, no. 01 (March 2008): 57–75. http://dx.doi.org/10.1142/s1469026808002144.

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This paper proposes a method to exchange information among multiple swarms in particle swarm optimization (PSO) to facilitate evolutionary search. The algorithm is developed to solve problems having landscapes with a large number of local optima. Each swarm maintains two sets of particles; one set includes the particles to be shared with other swarms, while the other involves the particles to be replaced by individuals from other swarms. The proposed algorithm also provides a new design to search for neighboring swarms in order to share common interests among the swarm's neighborhood. The particle's movement is according to one variation of PSO with three basic terms, each one to lead the particles toward the best particle in the swarm, in the neighborhood, and in the whole population. Demonstrated through a suite of benchmark test functions, the proposed algorithm shows competitive performance with improved convergence speed.

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Bozhinoski, Darko, and Mauro Birattari. "Towards an integrated automatic design process for robot swarms." Open Research Europe 1 (September 27, 2021): 112. http://dx.doi.org/10.12688/openreseurope.14025.1.

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Background: The specification of missions to be accomplished by a robot swarm has been rarely discussed in the literature: designers do not follow any standardized processes or use any tool to precisely define a mission that must be accomplished. Methods: In this paper, we introduce a fully integrated design process that starts with the specification of a mission to be accomplished and terminates with the deployment of the robots in the target environment. We introduce Swarm Mission Language (SML), a textual language that allows swarm designers to specify missions. Using model-driven engineering techniques, we define a process that automatically transforms a mission specified in SML into a configuration setup for an optimization-based design method. Upon completion, the output of the optimization-based design method is an instance of control software that is eventually deployed on real robots. Results: We demonstrate the fully integrated process we propose on three different missions. Conclusions: We aim to show that in order to create reliable, maintainable and verifiable robot swarms, swarm designers need to follow standardised automatic design processes that will facilitate the design of control software in all stages of the development.

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Liu, Hanmin, Qinghua Wu, and Xuesong Yan. "Relay Optimization Design Algorithm Based on Swarm Intelligence." Research Journal of Applied Sciences, Engineering and Technology 6, no. 1 (June 5, 2013): 165–70. http://dx.doi.org/10.19026/rjaset.6.4053.

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Quanxi Feng, Liu Sanyang, Zhang Jianke, and Yang Guoping. "Extrapolated particle swarm optimization based on orthogonal design." Journal of Convergence Information Technology 7, no. 2 (February 29, 2012): 141–52. http://dx.doi.org/10.4156/jcit.vol7.issue2.17.

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Yan, Xue Song, Qing Hua Wu, Cheng Yu Hu, and Qing Zhong Liang. "Circuit Design Based on Particle Swarm Optimization Algorithms." Key Engineering Materials 474-476 (April 2011): 1093–98. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1093.

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This work investigates the application of Particle Swarm Optimization (PSO) algorithms in the field of evolutionary electronics. PSO was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. PSO achieves its optimum solution by starting from a group of random solution and then searching repeatedly. We propose the new means for designing electronic circuits and introduce the modified PSO algorithm. For the case studies this means has proved to be efficient, experiments show that we have better results.

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Sarangi, Archana, Shubhendu Kumar Sarangi, Sasmita Kumari Padhy, Siba Prasada Panigrahi, and Bijay Ketan Panigrahi. "Swarm intelligence based techniques for digital filter design." Applied Soft Computing 25 (December 2014): 530–34. http://dx.doi.org/10.1016/j.asoc.2013.06.001.

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Zhu, Xiaoshu, Jie Zhang, and Junhong Feng. "Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/126404.

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In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform design, is proposed. The differences between the proposed algorithm and the others lie in that PAM and uniform design are firstly introduced to MOPSO. The experimental results performing on several test problems illustrate that the proposed algorithm is efficient.

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Dang, Hongtao, Yichen Du, Lingyun Kong, Hui Yao, and Jianxiang Xi. "Synchronization-Based Guaranteed-Performance Formation Design for Swarm Systems." Complexity 2020 (September 9, 2020): 1–13. http://dx.doi.org/10.1155/2020/3076132.

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Guaranteed-performance formation control for swarm systems with the second-order dynamics is investigated based on the synchronization control strategy. Firstly, a new formation protocol is presented, where the weights of connected edges are adaptively regulated and the performance constraint is imposed. Then, on the basis of the Riccati inequality, sufficient conditions for synchronization-based guaranteed-performance formation are proposed, and an explicit expression of the guaranteed-performance cost is shown, where it is fully distributed to design gain matrices of the formation protocol in the sense that it is independent of global information of swarm systems. Moreover, the whole motion of a swarm system is determined, which is associated with initial states of all agents and formation control vectors. Finally, two numerical examples are shown to demonstrate theoretical conclusions, where the static whole motion and the dynamic whole motion are considered, respectively.

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Asaamoning, Godwin, Paulo Mendes, Denis Rosário, and Eduardo Cerqueira. "Drone Swarms as Networked Control Systems by Integration of Networking and Computing." Sensors 21, no. 8 (April 9, 2021): 2642. http://dx.doi.org/10.3390/s21082642.

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The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.

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Garzón Ramos, David, and Mauro Birattari. "Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors." Applied Sciences 10, no. 13 (July 6, 2020): 4654. http://dx.doi.org/10.3390/app10134654.

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Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

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Cybulski, Piotr, and Zbigniew Zieliński. "UAV Swarms Behavior Modeling Using Tracking Bigraphical Reactive Systems." Sensors 21, no. 2 (January 17, 2021): 622. http://dx.doi.org/10.3390/s21020622.

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Recently, there has been a fairly rapid increase in interest in the use of UAV swarms both in civilian and military operations. This is mainly due to relatively low cost, greater flexibility, and increasing efficiency of swarms themselves. However, in order to efficiently operate a swarm of UAVs, it is necessary to address the various autonomous behaviors of its constituent elements, to achieve cooperation and suitability to complex scenarios. In order to do so, a novel method for modeling UAV swarm missions and determining behavior for the swarm elements was developed. The proposed method is based on bigraphs with tracking for modeling different tasks and agents activities related to the UAV swarm mission. The key finding of the study is the algorithm for determining all possible behavior policies for swarm elements achieving the objective of the mission within certain assumptions. The design method is scalable, highly automated, and problem-agnostic, which allows to incorporate it in solving different kinds of swarm tasks. Additionally, it separates the mission modeling stage from behavior determining thus allowing new algorithms to be used in the future. Two simulation case studies are presented to demonstrate how the design process deals with typical aspects of a UAV swarm mission.

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Araghi, Salman Khalili, Afshin Esmaeili, Gerald Hushlak, and Anna Hushlak. "Customizing Urban Pattern through an Agent-Based Approach." International Journal of Swarm Intelligence Research 5, no. 4 (October 2014): 33–44. http://dx.doi.org/10.4018/ijsir.2014100103.

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This paper discusses the 3D space customization of design concepts within self-generated sculpture as an instigator for design of urban pattern. Appropriating from the concept of computer fuzzy logic, fuzzy design prods serve as exemplars of naturally occurring swarm behaviors. The hybridization of design through the ‘mistake' and the different material vocabularies serve as departure points for the conceptualization of image breeding in 2D and for 3D grouping within urban pattern. Additive and eroding material processes spawn rule-based agent behaviors that assist the designers/artists to conceive and to enhance appearance and place. In an iterative process, swarm entities physically augment forms in an organic manner. The designer becomes the voyeur of their own creative input as swarm behaviors influence the placement and grouping of architecture/sculpture within the urban pattern of cities. In particular, this paper focuses on the agent-based approach whereby swarm behavior classifies residential, commercial and green spaces within urbanized areas.

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Li, Yan Bin, Tong Jiang, You Hua Gao, and Lian Yong Yu. "Design Optimization of Beam-Pumping Unit Based on Improved Particle Swarm Optimization." Applied Mechanics and Materials 130-134 (October 2011): 2540–43. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2540.

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A novel particle swarm optimization algorithm based on chaos searching has been proposed and introduced to optimize the design of beam pumping unit which takes the minimum peak torque factor on the up stroke as the objective function. The optimization for beam-pumping unit have shown the improved particle swarm optimization based on chaos searching algorithm has both advantage of simplify for implement of original particle swarm optimization and advantage of fast convergence, easy to escape local minima for chaos optimization algorithm. The improved optimization has also provided a new method for the design of complex structure.

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Fan, Jin Wei, Qin Mei, and Xiao Feng Wang. "Robust PID Parameters Optimization Design Based on Improved Particle Swarm Optimization." Applied Mechanics and Materials 373-375 (August 2013): 1125–30. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1125.

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The article, based on satisfying robustness of the system and put forward the objective function of time-domain performance and dynamic characteristics, introduced genetic operators into Particle Swarm Optimization. The algorithm improve the diversity of particles by selection and hybridization operations and strengthen the excellent characteristics of particles in the swarm by introducing crossover and mutation genes, which can avoid bog down into local optima and premature convergence and enhance searching efficiency. The simulation results indicate that when the algorithm is applied to the optimization of PID controller parameters of servo system of grinding wheel rack of MKS8332A CNC camshaft grinder, its performance is better than the single Genetic Algorithms or Particle Swarm Optimization, and it can also satisfy the demand of rapidity, stability and robustness.

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Perng, Jau Woei, Yi Shyang Huang, Shiang Shiuan Huang, Guan Yan Chen, Chin Yin Chen, and Ya Chao Yang. "SIWPSO-Based Controller Design for AUV." Applied Mechanics and Materials 525 (February 2014): 736–40. http://dx.doi.org/10.4028/www.scientific.net/amm.525.736.

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A strategy is proposed for a control system with a linearized autonomous underwater vehicle (AUV) dynamic model. The proposed approach combines the particle swarm optimization (PSO) and proportional-integral-derivative (PID) controller to adjust the parameters of the linearized dynamic model. The linear and nonlinear model are both considered in our work. The proposed techniques is verified by using the simulation results to the model of AUV.

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Mukhlish, Faqihza, John Page, and Michael Bain. "Reward-based epigenetic learning algorithm for a decentralised multi-agent system." International Journal of Intelligent Unmanned Systems 8, no. 3 (April 13, 2020): 201–24. http://dx.doi.org/10.1108/ijius-12-2018-0036.

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PurposeThis paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.Design/methodology/approachFirst, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.FindingsThrough various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.Originality/valueThis paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.

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Park, Ho-Sung, Ki-Sang Kim, and Sung-Kwun Oh. "Design of Particle Swarm Optimization-based Polynomial Neural Networks." Transactions of The Korean Institute of Electrical Engineers 60, no. 2 (February 1, 2011): 398–406. http://dx.doi.org/10.5370/kiee.2011.60.2.398.

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Mter, Adel H. AL, and Songfeng Lu. "A Particle Swarm Optimization Algorithm Based on Uniform Design." International Journal of Data Mining & Knowledge Management Process 6, no. 2 (March 30, 2016): 29–35. http://dx.doi.org/10.5121/ijdkp.2016.6203.

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ZHAI Zi-yu, 翟子羽, and 叶美盈 YE Mei-ying. "A Coating Design Method Based on Particle Swarm Optimization." ACTA PHOTONICA SINICA 40, no. 9 (2011): 1338–41. http://dx.doi.org/10.3788/gzxb20114009.1338.

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23

Wu, Qinghua, Hanmin Liu, and Xuesong Yan. "An improved design optimisation algorithm based on swarm intelligence." International Journal of Computing Science and Mathematics 5, no. 1 (2014): 27. http://dx.doi.org/10.1504/ijcsm.2014.059382.

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Bouallègue, Soufiene, Joseph Haggège, and Mohamed Benrejeb. "Particle swarm optimization-based fixed-structure ℋ ∞ control design." International Journal of Control, Automation and Systems 9, no. 2 (April 2011): 258–66. http://dx.doi.org/10.1007/s12555-011-0207-2.

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Vural, Revna Acar, Ozan Der, and Tulay Yildirim. "Particle swarm optimization based inverter design considering transient performance." Digital Signal Processing 20, no. 4 (July 2010): 1215–20. http://dx.doi.org/10.1016/j.dsp.2009.10.022.

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钟, 林钢. "Design of Drone Swarm Simulation Platform Based on Unity3D." Computer Science and Application 11, no. 09 (2021): 2242–51. http://dx.doi.org/10.12677/csa.2021.119229.

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Yang, Wenying, Jiuwei Guo, Yang Liu, and Guofu Zhai. "The Design of Contactors Based on the Niching Multiobjective Particle Swarm Optimization." Complexity 2018 (July 4, 2018): 1–10. http://dx.doi.org/10.1155/2018/9054623.

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Contactors are important components in circuits. To solve the multiobjective optimization problems (MOPs) of contactors, a niching multiobjective particle swarm optimization (NMOPSO) with the entropy weight ideal point theory is proposed in this paper. The new algorithm selecting and archiving the nondominated solutions based on the niching theory to ensure the diversity of the nondominated solutions. To avoid missing the extreme solutions of each objective during the multiobjective optimization process, extra particle swarms used to search the independent optimal solution of each objective are supplemented in this algorithm. In order to determine the best compromise solution, a method to select the compromise solution based on entropy weight ideal point theory is also proposed in this paper. Using the algorithm to optimize the characteristics of a typical direct-acting contactor, the results show that the proposed algorithm can obtain the best compromise solution in MOPs.

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Thangavel, M., S. Raghavan, R. Raviprakash, V. Rubesh Raja, and Shankar Manickam. "Design and Development of Swarm Robots for Security Applications." Applied Mechanics and Materials 110-116 (October 2011): 4757–64. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.4757.

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In this paper, a robot design suitable for swarm based security system is presented. First, a possible layout for swarm based security system is conceptualized and literatures related to mobile robot design are reviewed. Second, a set of conceptual designs for the swarm robot are evolved and discussed. Third, expressions are arrived for weight, rolling resistance, obstacle crossing ability and amount of slip based on robot geometric parameters. Fourth, the obstacle crossing ability for rear wheel driven and rear wheel driven robots are investigated. Fifth, a prototype is made and the analytical expressions arrived are verified experimentally. Sixth, the drive power required for the direct drive robot is determined. Finally, based on weight, number of actuators, type of drive, type of wheels used, rolling resistance, and obstacle crossing ability an appropriate robot design is selected.

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Zhou, Yong Quan, and Lingzi Liu. "An Effective Chaotic Cultural-Based Particle Swarm Optimization for Constrained Engineering Design Problems." Applied Mechanics and Materials 20-23 (January 2010): 64–69. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.64.

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In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.

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Tu, Shanshan, Obaid Rehman, Sadaqat Rehman, Shafi Khan, Muhammad Waqas, and Ajmal Farooq. "Optimization of Loneys Solenoid Design Using a Dynamic Search Based Technique." Applied Computational Electromagnetics Society 36, no. 1 (February 27, 2021): 35–40. http://dx.doi.org/10.47037/2020.aces.j.360105.

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Particle swarm optimizer is one of the searched based stochastic technique that has a weakness of being trapped into local optima. Thus, to tradeoff between the local and global searches and to avoid premature convergence in PSO, a new dynamic quantum-based particle swarm optimization (DQPSO) method is proposed in this work. In the proposed method a beta probability distribution technique is used to mutate the particle with the global best position of the swarm. The proposed method can ensure the particles to escape from local optima and will achieve the global optimum solution more easily. Also, to enhance the global searching capability of the proposed method, a dynamic updated formula is proposed that will keep a good balance between the local and global searches. To evaluate the merit and efficiency of the proposed DQPSO method, it has been tested on some well-known mathematical test functions and a standard benchmark problem known as Loney’s solenoid design.

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Ning, Zhou, and Zhang Jing. "Study on Mechanical Design Optimization Based on Improved Particle Swarm Optimization Algorithm." Open Mechanical Engineering Journal 9, no. 1 (October 7, 2015): 961–65. http://dx.doi.org/10.2174/1874155x01509010961.

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In view of local optimization in particle swarm optimization algorithm (PSO algorithm), chaos theory was introduced to PSO algorithm in this paper. Plenty of populations were generated by using the ergodicity of chaotic motion. The uniformly distributed initial particles of the particle swarms were extracted from the populations according to the Euclidean distance between particles, so that the particles could uniformly distribute in the solution space. Local search was carried out on the optimal position of the particles during evolution, so as to improve the development capability of PSO algorithm and prevent its prematurity, thus enhancing its global optimizing capability. Then the improved PSO algorithm was applied to mechanical design optimization. With optimization design for two-stage gear reducer as the study object, objective function and constraint conditions were determined by building a mathematical model of optimization design, thus realizing optimization design. Simulation and comparison between the improved algorithm and unimproved algorithm show that improved PSO algorithm can optimize the optimization results of PSO algorithm at a faster convergence rate.

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An, Feng, Si Cong Yuan, Wei Dong Yan, and Dong Hong Wang. "Multi-Objective Optimization Design for Gear Reducer Based on the Grey Particle Swarm Algorithm." Advanced Materials Research 631-632 (January 2013): 1044–50. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1044.

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Combining the thought of correlation degree analysis in the theory of grey, use of particle swarm algorithm, seeking it’s individual extreme value and global extreme value, and puts forward to the goal of mathematical model about more gray particle swarm optimization algorithm is presented, the algorithm is applied to speed reducer hoisting mechanism in the optimization of parameters. The optimization results show that the optimal parameters, than the original design of parameters for satisfactory results show the particle swarm optimization algorithm is used for gray hoisting mechanism optimized parameter design of gear reducer is effective and feasible.

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Salman, Muhammad, Antoine Ligot, and Mauro Birattari. "Concurrent design of control software and configuration of hardware for robot swarms under economic constraints." PeerJ Computer Science 5 (September 30, 2019): e221. http://dx.doi.org/10.7717/peerj-cs.221.

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Designing a robot swarm is challenging due to its self-organized and distributed nature: complex relations exist between the behavior of the individual robots and the collective behavior that results from their interactions. In this paper, we study the concurrent automatic design of control software and the automatic configuration of the hardware of robot swarms. We introduce Waffle, a new instance of the AutoMoDe family of automatic design methods that produces control software in the form of a probabilistic finite state machine, configures the robot hardware, and selects the number of robots in the swarm. We test Waffle under economic constraints on the total monetary budget available and on the battery capacity of each individual robot comprised in the swarm. Experimental results obtained via realistic computer-based simulation on three collective missions indicate that different missions require different hardware and software configuration, and that Waffle is able to produce effective and meaningful solutions under all the experimental conditions considered.

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Zhao, Jiangbin, Shubin Si, Zhiqiang Cai, Ming Su, and Wei Wang. "Multiobjective optimization of reliability–redundancy allocation problems for serial parallel-series systems based on importance measure." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 5 (May 2, 2019): 881–97. http://dx.doi.org/10.1177/1748006x19844785.

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For complex equipment, designers are challenged to reduce the expense while satisfying high requirements of system reliability. This article focuses on the multiobjective reliability–redundancy allocation problem for serial parallel-series systems to balance the conflicts between system reliability and design cost. The multiobjective reliability–redundancy allocation problem model for serial parallel-series systems is established with constraints on system reliability and design cost. An importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm is proposed to solve the multiobjective model effectively based on the importance measure–based harmony search and multiobjective particle swarm optimization algorithm. The performance of the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm is verified by comparison with the nondominated sorting genetic algorithm and importance measure–based multiobjective particle swarm optimization algorithm. In Experiment 1, the performance of the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm is better than that of the nondominated sorting genetic algorithm and importance measure–based multiobjective particle swarm optimization, and the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm also can get the Pareto front with better uniformity. Compared to the nondominated sorting genetic algorithm, four cases with different constraints of system reliability and design cost are considered in Experiment 2, and the importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm applies to the systems with the lower system reliability constraints and the higher design cost constraints.

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Cielo Borin, Lucas, Caio Ruviaro Dantas Osório, Gustavo Guilherme Koch, Thieli Smidt Gabbi, Ricardo Coração de Leão Fontoura de Oliveira, and Vinícius Foletto Montagner. "ROBUST CONTROL DESIGN PROCEDURE BASED ON PARTICLE SWARM OPTIMIZATION AND KHARITONOV'S THEOREM WITH AN APPLICATION FOR PMSMs." Eletrônica de Potência 25, no. 2 (June 28, 2020): 219–29. http://dx.doi.org/10.18618/rep.2020.2.0008.

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Chen, Dongdong, Jianxin Zhao, Chunlong Fei, Di Li, Yuanbo Zhu, Zhaoxi Li, Rong Guo, Lifei Lou, Wei Feng, and Yintang Yang. "Particle Swarm Optimization Algorithm-Based Design Method for Ultrasonic Transducers." Micromachines 11, no. 8 (July 23, 2020): 715. http://dx.doi.org/10.3390/mi11080715.

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In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(ZrxTi1−x)O3 (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.

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Yao, Xiao Ling, and Yan Ni Wang. "A Complicated Transfer Design Method Based on Particle Swarm Optimization." Applied Mechanics and Materials 729 (January 2015): 208–12. http://dx.doi.org/10.4028/www.scientific.net/amm.729.208.

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Based on the simple transfer function design, this thesis presents a technology for complicated transfer function design. It converts complicated transfer function design problem into the fusing of several simple transfer function. The keystone is to formulate the transfer function fusing problem into searching for optimal fusing proportion, and to generate the new fusing proportion using a similarity evaluation method, which is based on expectation fitness. To a large extent, it simplifies the design process of complicated transfer function.

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Liu, Han Min, Qing Hua Wu, and Xue Song Yan. "MEMS Relay Optimization Design Algorithm Based on Particle Swarm Optimization." Key Engineering Materials 562-565 (July 2013): 155–61. http://dx.doi.org/10.4028/www.scientific.net/kem.562-565.155.

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Mathematical model of the MEMS relay volume involves in mechanical, electrical, magnetic, thermal, etc., the MEMS relay optimization design is a constrained nonlinear function optimization problem. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solutions when compared to other heuristic methods and is a powerful optimization algorithm for MEMS relay optimization design.

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Hu, Tian-lei, Gang Chen, Xiao-yan Li, and Jin-xiang Dong. "Automatic relational database compression scheme design based on swarm evolution." Journal of Zhejiang University-SCIENCE A 7, no. 10 (October 2006): 1642–51. http://dx.doi.org/10.1631/jzus.2006.a1642.

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Baliarsingh, A. K. "DESIGN OF TCSC-BASED CONTROLLER USING PARTICLE SWARM OPTIMIZATION ALGORITHM." IOSR Journal of Engineering 02, no. 04 (April 2012): 810–13. http://dx.doi.org/10.9790/3021-0204810813.

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Gaby, G., D. Prayogo, B. H. Wijaya, F. T. Wong, and D. Tjandra. "Reliability-based Design Optimization for Structures Using Particle Swarm Optimization." Journal of Physics: Conference Series 1625 (September 2020): 012016. http://dx.doi.org/10.1088/1742-6596/1625/1/012016.

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Izui, Kazuhiro, Shinji Nishiwaki, and Masataka Yoshimura. "415 Structural Optimization Using Swarm Algorithm Based on Design Sensitivities." Proceedings of Conference of Kansai Branch 2005.80 (2005): _4–35_—_4–36_. http://dx.doi.org/10.1299/jsmekansai.2005.80._4-35_.

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Imae, Joe, Amuro Araki, Tomoaki Kobayashi, and Guisheng Zhai. "1117 GP-Based Emergent Control System Design Incorporating Swarm Behavior." Proceedings of Conference of Kansai Branch 2007.82 (2007): _11–17_. http://dx.doi.org/10.1299/jsmekansai.2007.82._11-17_.

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Ho, S. L., Shiyou Yang, Guangzheng Ni, E. W. C. Lo, and H. C. Wong. "A particle swarm optimization-based method for multiobjective design optimizations." IEEE Transactions on Magnetics 41, no. 5 (May 2005): 1756–59. http://dx.doi.org/10.1109/tmag.2005.846033.

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Chen, Ti-Hung, Ming-Feng Yeh, and Wen-Yo Lee. "Particle swarm optimization based networked control system design with uncertainty." MATEC Web of Conferences 119 (2017): 01051. http://dx.doi.org/10.1051/matecconf/201711901051.

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Kumar, Anil, Nikhil Agrawal, Ila Sharma, Seungchan Lee, and Heung-No Lee. "Hilbert Transform Design Based on Fractional Derivatives and Swarm Optimization." IEEE Transactions on Cybernetics 50, no. 5 (May 2020): 2311–20. http://dx.doi.org/10.1109/tcyb.2018.2875540.

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Xu, Linsen, Wei Yang, and Hongxian Tian. "Design of Wideband CIC Compensator Based on Particle Swarm Optimization." Circuits, Systems, and Signal Processing 38, no. 4 (September 26, 2018): 1833–46. http://dx.doi.org/10.1007/s00034-018-0947-9.

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Kim, Dong Hun. "A swarm system design based on a modified particle swarm algorithm for a self-organizing scheme." Advanced Robotics 20, no. 8 (January 2006): 913–32. http://dx.doi.org/10.1163/156855306777951456.

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Wu, Chang Wei, Yong Hai Wu, Cong Bin Ma, and Cheng Wang. "Optimization Design of Gear Train Based on Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 373-375 (August 2013): 1072–75. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1072.

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Particle swarm optimization algorithms have lots of advantages such as fast convergence speed, good quality of solution and robustness in multidimensional space function optimization and dynamic target optimization. It is suitable for structural optimization design. In this paper, manual transmission gear train of a tractor is taken as research object, the minimum quality and minimum center distance of the gear train is taken as optimization goal, the gear ratio, modulus, helix angle, tooth width and equilibrium conditions of the axial force are taken as the constraints, a multi-objective optimization model of the gear train is established. The optimal structure design programs and Pareto optimal solution are obtained by using particle swarm optimization algorithm.

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Pan, Haipeng, and Dongbin Jin. "Design and Application of Variable Universe Fuzzy Controller Based on Cat Swarm Optimization." Mathematical Problems in Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/4632064.

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A novel variable universe fuzzy controller based on cat swarm optimization (CSO-VUFC) is proposed to regulate the temperature of the reactor system, which is characterized by nonlinearity, large time delay, and uncertainty. In CSO-VUFC, firstly, corresponding contraction-expansion factors with the function form were, respectively, introduced for the input and output fuzzy universes of the controller. Then, cat swarm optimization was used to optimize the relevant parameter values in the contraction-expansion factor function to achieve the intelligence optimization of the contraction-expansion factors, based on the system performance test function as an evaluation index; the contradiction between the universe adjustment and control accuracy of the fuzzy controller will be effectively solved to achieve the online self-adjustment of the universe. The simulation results indicate that the variable universe adaptive fuzzy control method based on the cat swarm optimization has the features of high precision adjustment, short transient time, and hard real-time.

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