Traditional optimization methods applied to solve real life problems can experience local or unstable behavior. Researchers proposed recently non-traditional search and optimization methods based on natural phenomena like natural evolution, social behavior, and annealing. This paper considers migration algorithms and bees’ algorithms as good candidates to solve multi-objective optimization problems.

This paper presents recent approaches in subjective data analysis by soft computing techniques to manage imprecision and uncertainty which appear in software reliability engineering. Firstly, recent approaches like imprecise probabilities, generalized intervals, fuzzy sets and intuitionistic-fuzzy sets are shortly described, and the usage of intuitionistic fuzzy numbers for system reliability computation is shown. Intuitionistic-fuzzy approaches

Software architecture is considered as a critical design methodology for the development of complex software. As an important step in software quality assurance, the optimal reliability allocation for software projects can be obtained by minimizing the total cost of achieving the target reliability or maximizing the system reliability subject to budget constraints. These kinds of optimization problems were considered both in deterministic and stochastic

Digital learning objects are powerful units for building learning, education or training materials based on ICT recent developments. Not only text and photos, but also audio, video, and simulation units are used to build high quality e-lessons (available online), or blended learning resources. There are a large plethora of types of digital resources used in e-Learning content development, and for every category a particular set of criteria are used

This paper describes the most important characteristics of cloud computing paradigm in order to be used as powerful arrows towards efficient approaches in teaching data analysis. Also High Performance Computing aspects are considered for solving large complexity data analysis problems. The usage of both Cloud Computing and High Performance Computing will assure not only an excellent framework for research but also a powerful and flexible environment

This paper presents recent approaches in soft computing to manage imprecision and uncertainty in software reliability engineering. Firstly, recent approaches like imprecise probabilities, generalized intervals, fuzzy sets and intuitionistic-fuzzy sets are shortly described, and the usage of intuitionistic fuzzy numbers for system reliability computation is shown. Intuitionistic-fuzzy approaches for software reliability growth models are proposed

Quality evaluation of learning objects asks for subjective approaches in a multi-criteria framework. This paper describes the usage of intuitionistic fuzzy sets for modelling the degree of acceptance/rejection of learning objects according to a set of criteria and proposes some methods for the best learning object identification. Experimental results are reported for items belonging to the computer science field evaluated both by students and teaching

This paper considers the software risk management using intuitionistic-fuzzy models and imprecise/fuzzy probabilities or alternatives. The approach can be used for those cases when small data collections are available for analysis under vague and imprecise information based on expert information given by fuzzy numbers and/or fuzzy rules. The risk management for component based software development, agile management, and software security by analysis

In this paper we describe an interesting approach to error prediction illustrated by experimental results. The application consists of monitoring the activity for the web servers in order to collect the specific data. Predicting an error with severe consequences for the performance of a server (the result of which is that its functionality becomes totally inaccessible or hard to access for clients) requires measuring the capacity of a server at any