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Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.


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Journal ArticleDOI
TL;DR: The proposed methodology aims to bring methodological support to scenario-based decision making in scenario analysis by combining Delphi method, soft computing (fuzzy cognitive maps) and multicriteria (TOPSIS) techniques.
Abstract: Highlights? The methodology proposed is a step forward with regard to the classic tools used in scenarios. ? The proposed approach that it aims to use the scenarios' assessment and ranking as a whole. ? The proposal combine Delphi method, soft computing (fuzzy cognitive maps) and multicriteria (TOPSIS) techniques. Scenarios describe events and situations that would occurred in the future real-world. Policy makers use scenario methods as a tool to build landscapes of possible futures at a national level. Based on these future visions, policy and decision-makers are able to explore different courses of action. In recent years, the number of potential scenario methods and applications is increasing. It is because academics and practitioners are increasing their interest about it. In spite of the success of scenario methods' support, scenario-based decision making still is not a fully structured process.The proposed methodology aims to bring methodological support to scenario-based decision making in scenario analysis. The originality of the proposed approach with respect to other ones is that it aims to use the scenarios' assessment and ranking as a whole. Traditional approaches consider the future impact of each present entity in isolation. This assumption is a simplification of a more complex reality, in which different entities interact with each other. The model that the authors propose allows decision and policy makers to measure the impact of a entity interactions. To reach this aim, the proposal combine Delphi method, soft computing (fuzzy cognitive maps) and multicriteria (TOPSIS) techniques. In addition, a numerical example is developed for illustrating the proposal.

95 citations

Journal Article
TL;DR: In this article, a generalization of Molodtsov's soft set, called soft multiset, is introduced, with its basic operations such as complement, union and intersection.
Abstract: In 1999 Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. The solutions of such problems involve the use of mathematical principles based on uncertainty and imprecision. In this paper we recall the definition of a soft set, its properties and its operations. As a generalization of Molodtsov’s soft set we introduce the definitions of a soft multiset, its basic operations such as complement, union and intersection. We give examples for these concepts. Basic properties of the operations are also given.

95 citations

Proceedings ArticleDOI
12 Jul 2011
TL;DR: IACOR-LS as discussed by the authors is a variant of ACOR that uses local search and features a growing solution archive, which is a significant improvement over ACOR, but it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal.
Abstract: ACOR is one of the most popular ant colony optimization algorithms for tackling continuous optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on continuous optimization problems.

95 citations

Journal ArticleDOI
Yu-Chi Ho1
TL;DR: This tutorial explains the fundamentals of ordinal optimization, a tool for computationally intensive system performance evaluation and optimization problems, and argues its inclusion in the arsenal of soft computing techniques.

94 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023159
2022270
2021319
2020332
2019313
2018348