Topic
Shrinking space
About: Shrinking space is a research topic. Over the lifetime, 17 publications have been published within this topic receiving 123 citations.
Papers
TL;DR: In this paper, a differentiated model for a regulatory framework based on functional roles is proposed to accommodate a much more diverse and expanded set of civil society organizations (CSO), and the authors also derive initial recommendations on how governments and civil society could find ways to relate to each other in both national and multilateral contexts.
Abstract: The relationship between many G20 governments and organized civil society has become more complex, laden with tensions, and such that both have to find more optimal modes of engagement. In some instances, state-civil society relations have worsened, leading some experts and activists to speak of a “shrinking space” for civil society. How wide-spread is this phenomenon? Are these more isolated occurrences or indeed part of a more general development? How can countries achieve and maintain an enabling environment for civil society? The authors suggest that much of the current impasse results foremost from outdated and increasingly ill-suited regulatory frameworks that fail to accommodate a much more diverse and expanded set of civil society organizations (CSO). In response, they propose a differentiated model for a regulatory framework based on functional roles. Based on quantitative profiling and expert surveys, moreover, the paper also derives initial recommendations on how governments and civil society could find ways to relate to each other in both national and multilateral contexts.
66 citations
TL;DR: This research presents a constrained evolutionary algorithm with high efficiency and accuracy for constrained optimization problems and introduces three different mutant strategies to generate different offspring into evolutionary population.
Abstract: Most of the current evolutionary algorithms for constrained optimization algorithm are low computational efficiency. In order to improve efficiency, an improved differential evolution with shrinking space technique and adaptive trade-off model, named ATMDE, is proposed to solve constrained optimization problems. The proposed ATMDE algorithm employs an improved differential evolution as the search optimizer to generate new offspring individuals into evolutionary population. For the constraints, the adaptive trade-off model as one of the most important constraint-handling techniques is employed to select better individuals to retain into the next population, which could effectively handle multiple constraints. Then the shrinking space technique is designed to shrink the search region according to feedback information in order to improve computational efficiency without losing accuracy. The improved DE algorithm introduces three different mutant strategies to generate different offspring into evolutionary population. Moreover, a new mutant strategy called “DE/rand/best/1” is constructed to generate new individuals according to the feasibility proportion of current population. Finally, the effectiveness of the proposed method is verified by a suite of benchmark functions and practical engineering problems. This research presents a constrained evolutionary algorithm with high efficiency and accuracy for constrained optimization problems.
21 citations
10 citations
1 Jan 2019
6 citations
1 Jan 2013
5 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2019 | 4 |
| 2018 | 1 |
| 2017 | 3 |
| 2016 | 1 |
| 2013 | 1 |