Khare et al., 2023 - Google Patents
An overview of swarm intelligence-based algorithmsKhare et al., 2023
- Document ID
- 16269750074457146881
- Author
- Khare O
- Ahmed S
- Singh Y
- Publication year
- Publication venue
- Design and Applications of Nature Inspired Optimization: Contribution of Women Leaders in the Field
External Links
Snippet
Since the inception and introduction of swarm intelligence (SI)-based algorithms to the field of optimization, these methods have emerged as an effective tool to deal with increasingly complex problems. Their prominence and success have been attributed to their self-learning …
- 238000005457 optimization 0 abstract description 39
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Programme initiating; Programme switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
- G06N5/043—Distributed expert systems, blackboards
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/28—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for programming tools or database systems, e.g. ontologies, heterogeneous data integration, data warehousing or computing architectures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/44—Arrangements for executing specific programmes
- G06F9/4421—Execution paradigms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/004—Artificial life, i.e. computers simulating life
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fister Jr et al. | A brief review of nature-inspired algorithms for optimization | |
| Khare et al. | An overview of swarm intelligence-based algorithms | |
| Sarkheyli et al. | The role of basic, modified and hybrid shuffled frog leaping algorithm on optimization problems: a review | |
| Mafarja et al. | An efficient high-dimensional feature selection approach driven by enhanced multi-strategy grey wolf optimizer for biological data classification | |
| Su et al. | Dove swarm optimization algorithm | |
| Hu et al. | Swarm intelligence-based optimisation algorithms: an overview and future research issues | |
| Kumar et al. | Hybridization of magnetic charge system search and particle swarm optimization for efficient data clustering using neighborhood search strategy | |
| Yang et al. | Swarm intelligence in data science: applications, opportunities and challenges | |
| Hassan et al. | Enhancement of health care services based on cloud computing in IOT environment using hybrid swarm intelligence | |
| Baalamurugan et al. | An efficient clustering scheme for cloud computing problems using metaheuristic algorithms | |
| Chinglemba et al. | Introductory review of swarm intelligence techniques | |
| Odılı et al. | A critical review of major nature-inspired optimization algorithms | |
| Singh et al. | Scheduling in cloud computing environment using metaheuristic techniques: a survey | |
| Karimunnisa et al. | Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing. | |
| Milošević et al. | Intelligent process planning for smart factory and smart manufacturing | |
| Damaševičius et al. | State flipping based hyper-heuristic for hybridization of nature inspired algorithms | |
| Reddy et al. | Software effort estimation using particle swarm optimization: Advances and challenges | |
| Burgin et al. | Structural machines and slime mould computation | |
| Zhang et al. | Design and development of a unified framework towards swarm intelligence | |
| Tao et al. | Brief history and overview of intelligent optimization algorithms | |
| Wu et al. | A dynamic multistage hybrid swarm intelligence optimization algorithm for function optimization | |
| Agrawal et al. | A review of spider monkey optimization: modification and its biomedical application | |
| Khan et al. | Analysis of different approach used in particle swarm optimization variants: A comparative study | |
| Bharti et al. | Swarm intelligence for deep learning: Concepts, challenges and recent trends | |
| Xing et al. | Bacteria inspired algorithms |