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Social Learning Optimization
| Content Provider | Scilit |
|---|---|
| Author | Gong, Yue-Jiao |
| Copyright Year | 2021 |
| Description | This chapter discusses the novel optimization approach that mimics the social learning process of humans. It describes a novel algorithm from the perspective of mimicking the social learning process of humans, which was first developed by Gong et al. in 2014. According to the social learning theory, the observational learning process consists of four basic steps: attention, retention, reproduction, and motivation. The experiments and results in this chapter will validate the good performance of social learning optimization (SLO), in terms of global search ability, local exploitation, and efficiency. Based on this observation, a novel optimization technique termed the SLO has been developed, which emulates the social intelligence of humans in computers. The attention symbol will be used to guild the subsequent reproduction and reinforcement procedures. The main process includes four basic operators, i.e., attention, reproduction, reinforcement, and motivation, based on which the society converges to the optimum of the problem at hand. Book Name: Handbook of AI-based Metaheuristics |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003162841-13&type=chapterpdf |
| Ending Page | 282 |
| Page Count | 20 |
| Starting Page | 263 |
| DOI | 10.1201/9781003162841-13 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-07-12 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Handbook of Ai-based Metaheuristics Optimization Slo Reinforcement Learning Process Social Learning Reproduction Chapter Motivation Basic |
| Content Type | Text |
| Resource Type | Chapter |
