Soft computing

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

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Abstract: Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web’s largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

A survey of the application of soft computing to investment and financial trading

Abstract: This paper surveys recent literature in the domain of applying Soft Computing to Investment and Financial Trading. It analyses the literature according to the style of soft computing used, the investment discipline used, the successes demonstrated, and the applicability of the research to real world trading. This papers contribution is to expose the key areas where research is being undertaken, and to attempt to quantify the degree of successes associated with the different research approaches.

Neuro-fuzzy feature selection approach based on linguistic hedges for medical diagnosis

Abstract: The use of soft computing techniques in disease diagnosis is increasing. This is mainly because the effectiveness of classification and prediction systems has been improved to help physicians in diagnosing. One of the core issues in medical data analysis and mining is the curse of dimensionality; particularly, the medical datasets are characterised by relatively few instances and presented in a high-dimensional feature space. Feature selection plays an important role in building classification systems. It can not only reduce the dimension of data, but also lower the computation costs and gain a good classification performance. In this paper, a feature selection method based on linguistic hedges neural-fuzzy classifier (LHNFC) is presented for medical diagnosis. This classifier is used to achieve very fast, simple and efficient diagnosis. The results indicated that use of linguistic hedges in adaptive neural-fuzzy classifier improves the success of the classifier. Applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only helps to reduce the dimensionality of large datasets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

Performance Analysis of MPPT Techniques for Dynamic Irradiation Condition of Solar PV

Abstract: Solar Photovoltaic (PV) systems are playing a major role in the present electrical energy systems. The solar PV gives nonlinear I–V and P–V characteristics. As a result, it is difficult to extract the maximum power of the solar PV. Under Partial Shading Conditions (PSCs), the solar PV characteristics consist of multiple local Maximum Power Points (MPPs) and one global MPP. The classical Maximum Power Point Tracking (MPPT) techniques cannot track the global MPP under PSCs. Accordingly, this work aims to study the performance of five soft computing MPPT techniques. The studied five soft computing MPPT techniques are Modified Variable Step Size-Radial Basis Functional Network (MVSS-RBFN), Modified Hill-Climb with Fuzzy Logic Controller (MHC-FLC), Artificial Neuro-Fuzzy Inference System (ANFIS), Perturb and Observe with Practical Swarm Optimization (P&O-PSO), and Adaptive Cuckoo Search (ACS). The comparative performance analysis of five soft computing techniques has been carried out against the Variable Step Size-Incremental Resistance (VSS-INR), and Variable Step Size-Feedback Controller (VSS-FC)-based MPPT techniques. The performance analysis of seven MPPT techniques has been done by considering the parameters are steady-state settling time, MPP tracking speed, algorithm complexity, PV array dependency, handling of partial shading, and efficiency.