检索结果-内蒙古大学图书馆

swarm algorithms for single- and multi-objective optimization problems incorporating sensitivity analysis

ENGINEERING OPTIMIZATION 2007年第8期39卷 981-998页

作者： Izui, K. Nishiwaki, S. Yoshimura, M. Kyoto Univ Dept Aeronaut & Astronaut Sakyo Ku Kyoto 6068501 Japan

swarm algorithms such as particle swarm optimization (PSO) are non-gradient probabilistic optimization algorithms that have been successfully applied for global searches in complex problems such as multi-peak problems. However, application of these algorithms to structural and mechanical optimization problems still remains a complex matter since local optimization capability is still inferior to general numerical optimization methods. This article discusses new swarm metaphors that incorporate design sensitivities concerning objective and constraint functions and are applicable to structural and mechanical design optimization problems. Single- and multi-objective optimization techniques using swarm algorithms are combined with a gradient-based method. In the proposed techniques, swarm optimization algorithms and a sequential linear programming (SLP) method are conducted simultaneously. Finally, truss structure design optimization problems are solved by the proposed hybrid method to verify the optimization efficiency.

关键词： swarm algorithms particle swarm optimization global search multi-objective optimization design sensitivity structural optimization

来源：评论

学校读者我要写书评

暂无评论

Capitalizing Diversity for Efficiency Enhancement in Multi-Population swarm algorithms 10

Capitalizing Diversity for Efficiency Enhancement in Multi-P...

引用

10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

ISBN: (纸本)9781538659069

swarm algorithms are an efficient means to optimize various real-life problems. Their efficiency is influenced by diversity, which helps it to escape any local optima. There are multiple ways to increase diversity like mutation, repulsion, multi-population, replacement, etc. Of these, multi-population and replacement based techniques work without changing the internal functioning of any algorithm. In this paper we study three different replacement based techniques for multi-population swarm algorithms. The paper also proposes techniques (variant/new) to improve diversity in a multi-population scenario. All the techniques:-studied and proposed are subsequently tested with bat algorithm over 30 benchmark functions. The most efficient amongst them is further tested over six other swarm algorithms. The comparative results indicate that the identified technique is significantly better in terms of efficiency and diversity for most algorithms. Hence it can be concluded that it is an efficient means to improve the diversity and efficiency of a multi-population swarm algorithm.

关键词： diversity enhancement multi-population swarm algorithms

来源：评论

学校读者我要写书评

暂无评论

Induction machine fault identification using particle swarm algorithms

Induction machine fault identification using particle swarm ...

引用

IEEE International Conference on Power Electronic, Drives and Energy Systems

作者： Ethny, S. A. Acarnley, P. P. Zahawi, B. Giaouris, D. Univ Newcastle Sch Elect Elect & Comp Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9780780397712

The principles of a new technique using particle swarm algorithms for condition monitoring of the stator and rotor circuits of an induction machine is described in this paper. Using terminal voltage and current data, the stochastic optimization technique is able to indicate the presence of a fault and provide information about the location and nature of the fault. The technique is demonstrated using experimental data from a laboratory machine with both stator and rotor winding faults.

关键词： condition monitoring induction machine stochastic optimization swarm algorithms

来源：评论

学校读者我要写书评

暂无评论

Study of population partitioning techniques on efficiency of swarm algorithms

引用

swarm AND EVOLUTIONARY COMPUTATION 2020年 55卷 100672-100672页

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

This paper presents a study of various population partitioning techniques and their effect on the efficiency of swarm algorithms. Population partitioning techniques based on different concepts have been studied. Prominent amongst them is self-adaptive multi-population (SAMP) technique where populations are added and deleted dynamically based on their diversity. This techniques start with a single randomly initialised population, called free population. After evolution, if the distance between solutions drops below a limit, it is considered to have converged. If all existing populations have converged, a new randomly generated population is added. SAMP keeps at least one free population at all times, hence ensuring the algorithm doesn't get trapped in local optima. Another promising population partitioning technique studied is random partitioning, where a single population is divided into many smaller sub-populations randomly. Few extensions to the studied techniques are proposed, like an adaptive hierarchical partitioning technique, seed based partitioning with fixed seeds, random partitioning with master population, SAMP with random partitioning etc. All the studied and proposed techniques are compared over a set of benchmark functions. The strongest amongst all techniques was found to be SAMPR. SAMPR is a hybrid of self-adaptive multi-population (SAMP) technique and random partitioning where after every few generations all populations are combined together and re-partitioned randomly. Efficiency of SAMPR is validated over seven well-known swarm algorithms. Extensive comparisons are conducted over multiple benchmark functions, CEC'14 function set and 800 GKLS generated functions. Results establish the efficiency of the proposed technique for improving performance of swarm algorithms.

关键词： Multi-population Population partitioning swarm algorithms Global optimisation

来源：评论

学校读者我要写书评

暂无评论

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and swarm algorithms

引用

SENSORS 2022年第14期22卷 5317-5317页

作者： Pathmakumar, Thejus Muthugala, M. A. Viraj J. Samarakoon, S. M. Bhagya P. Gomez, Braulio Felix Elara, Mohan Rajesh Singapore Univ Technol & Design Engn Prod Dev Singapore 487372 Singapore

Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region's cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

关键词： audit robot geometrical feature cleaning auditing swarm algorithms

来源：评论

学校读者我要写书评

暂无评论

Transparent and Efficient Parallelization of swarm algorithms

引用

ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS 2016年第2期11卷 14-14页

作者： Cicirelli, Franco Forestiero, Agostino Giordano, Andrea Mastroianni, Carlo CNR Inst High Performance Comp & Networks Via P Bucci 41C I-87036 Arcavacata Di Rende CS Italy

This article presents an approach for the efficient and transparent parallelization of a large class of swarm algorithms, specifically those where the multiagent paradigm is used to implement the functionalities of bioinspired entities, such as ants and birds. Parallelization is achieved by partitioning the space on which agents operate onto multiple regions and assigning each region to a different computing node. Data consistency and conflict issues, which can arise when several agents concurrently access shared data, are handled using a purposely developed notion of logical time. This approach enables a transparent porting onto parallel/distributed architectures, as the developer is only in charge of defining the behavior of the agents, without having to cope with issues related to parallel programming and performance optimization. The approach has been evaluated for a very popular swarm algorithm, the ant-based spatial clustering and sorting of items, and results show good performance and scalability.

关键词： algorithms Performance swarm algorithms logical time ant-based clustering and sorting

来源：评论

学校读者我要写书评

暂无评论

Random Exploration and Attraction of the Best in swarm Intelligence algorithms

引用

APPLIED SCIENCES-BASEL 2024年第23期14卷 11116页

作者： Vargas, Maria Cortes, Domingo Ramirez-Salinas, Marco Antonio Villa-Vargas, Luis Alfonso Lopez, Antonio Inst Politecn Nacl ESIME Culhuacan Ave St Ana 1000 Mexico City 04440 Mexico Inst Politecn Nacl Ctr Invest Comp Ave Juan Dios Batiz Mexico City 07738 Mexico

In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures;that is, to build algorithms to make a wide and efficient explorations of the search space and then gradually make that the best-evaluated search agent to attract the rest of the swarm. Following this general idea, two REAB-based algorithms are proposed;one derived from the PSO and one derived from the GWO, called REAB-PSO and REAB-GWO, respectively. To easily and succinctly express both algorithms, variable-sized open balls are employed. A comparison of proposed procedures in this paper and the original PSO and GWO using a controller tuning problem as a test bench show a significant improvement of the REAB-based algorithms over their original counterparts. Ideas here exposed can be used to derive new swarm intelligence algorithms.

关键词： metaheuristic optimization swarm algorithms algorithm development controller tuning optimization

来源：评论

学校读者我要写书评

暂无评论

Comprehensive performance metric for bio-inspired optimizers: a generation-wise population convergence approach toward global optimality

引用

Iran Journal of Computer Science 2025年 1-49页

作者： Thapliyal, Shivankur Kumar, Narender Doon University Dehradun India

Metaheuristic algorithms revolutionize optimization, offering flexibility, scalability, and problem-solving expertise. They excel in diverse domains due to adaptability, parallelism, and global exploration, making them essential in modern computing. Researchers have created numerous metaheuristics, necessitating benchmarking and comparisons to comprehend their capabilities and limitations, considering the “No Free Lunch Theorem”. Despite their prominence, the fundamental questions regarding performance difficulties are only partially answered due to a lack of in-depth analytical analysis. Due to the limitations of conventional metrics such as convergence rate and the quality of the final solution, which often do not provide a comprehensive understanding of an algorithm’s performance and can sometimes be confusing or challenging to interpret. This study introduces a new performance metric that monitors the algorithm’s behavior, including exploration, exploitation, and convergence, across iterations while considering data from all individuals in the population to rectify these limitations. The evaluation of the proposed matrices involves three stages. First, the performance of ten metaheuristic algorithms is analyzed using the proposed metric on 23 benchmark functions with dimensions of 30, 100, 500, and 1000, as well as 30 functions from IEEE CEC-2017 with dimensions of 30 and 15 functions from IEEE CEC-2015 with dimensions of 10. Second, the metric is applied to assess multi-layer perceptron adaptations on eight manifold datasets. Finally, graphical and statistical tools, including error trajectory plots, rating matrices, and Friedman ranking tests, are utilized to provide deeper insights. The results demonstrate the metric’s effectiveness in evaluating the exploration, exploitation, and convergence abilities of the algorithms. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Benchmark functions Bio-inspired algorithms Metaheuristics Nature-inspired algorithms Optimization Particle swarm optimization Performance metric swarm algorithms

来源：评论

学校读者我要写书评

暂无评论

A review of optimization swarm intelligence-inspired algorithms with type-2 fuzzy logic parameter adaptation

引用

SOFT COMPUTING 2020年第1期24卷 215-226页

作者： Valdez, Fevrier Tijuana Inst Technol Tecnol Nacl Mexico Tijuana BC Mexico

In this paper, a survey about the algorithms based on swarm intelligence with parameter adaptation using some techniques to achieve the best results is presented. In this case, we analyzed the most popular algorithms such as ant colony optimization, particle swarm optimization, bee colony optimization, bat algorithm, firefly algorithm and cuckoo search. These algorithms are referenced in the paper because they have demonstrated to be superior with respect to the other optimization methods based on swarms with parameter adaptation using type-2 fuzzy logic in some applications, and also the algorithms are inspired on swarm intelligence.

关键词： Optimization methods Collective intelligence swarm algorithms Parameter adaptation Type-2 fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Current algorithms, Communication Methods and Designs for Underwater swarm Robotics: A Review

引用

IEEE SENSORS JOURNAL 2021年第1期21卷 153-169页

作者： Connor, Jack Champion, Benjamin Joordens, Matthew A. Deakin Univ Sch Engn Geelong Vic 3216 Australia

As technology advances, the places we have been able to explore have drastically increased. However, the advancements in the underwater realm have staggered behind both the exploration of surface and air domains. This is due in part to the challenges that arise when placing a robot in water. One current shift has seen the use of a swarm of robots that are cheaper and are of a lower quality, that work together to accomplish a common goal, as opposed to using a single expensive robot. swarm robotics benefits from being more tolerant of catastrophic failure and can cover large areas in smaller time frames. However, unlike other advancements in technology, underwater swarm robotics have struggled to compete with its counterparts on the surface and in the air. This is mainly due to the problems with communication underwater, the hazardous environment, the cost and difficulties with construction of underwater robots. This article conducts a literature review into the current state of underwater swarm robotics;it covers the design of the underwater robots, the methods used by the individual robot to perceive their environment, how they can localize to said environment, the methods of communication available underwater, centralized and decentralized control, the basis of swarm algorithms, the behaviors that are exhibited when a swarm works collectively and how swarms have been applied underwater.

关键词： Fish Propellers Unmanned underwater vehicles Robot sensing systems swarm algorithms swarm behaviors swarm robotics underwater robotics underwater communication

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：