The study of evolutionary algorithms (EAs) has witnessed an impressive increase during the last decades. The need to explore this area is determined by the growing request for design and the optimization of more and more engineering problems in society, such as highway construction processes, food and agri-technologies processes, resource allocation problems, logistics and transportation systems, microarchitectures, suspension systems optimal design, etc. All of these matters refer to specific highly computational problems with a huge design space, hence the obvious need for evolutionary algorithms and frameworks, or platforms that allow for the implementing and testing of such algorithms and methods. This paper aims to comparatively analyze the existing software platforms and state-of-the-art multi-objective optimization algorithms and make a review of what features exist and what features might be included next as further developments in such tools, from a researcher’s perspective. Additionally, it is essential for a framework to be easily extendable with new types of problems and optimization algorithms, metrics and quality indicators, genetic operators or specific solution representations and results analysis and comparison features. After presenting the most relevant existing features in these types of platforms, we suggest some future steps and the developments we have been working on.

The need for high-availability, efficient, training solutions for industrial automation technology is driving the adoption of new tools, software and services supporting the Industry 4.0 paradigm. Specific requirements include vendor agnostic and standards-based programming languages and availability of both physical input–output and industrial communication, while providing a rich and immersive experience through suitable human–machine interface components. The PROSIM platform represents an integrated embedded hardware-software system for programmable logic controller automation training which provides examples in increasing order of complexity, ranging from basic digital logic up to complex manufacturing simulations and process control. A fully software-based solution has been developed in order to complement the laboratory-based system as a training digital twin. We describe the design, implementation and evaluation of the platform as ported onto the HUBCAP collaborative sandboxing environment for virtualized operation. Integration of dedicated communication libraries for Open Platform Communications, Unified Architecture, with simulated programmable logic controllers such as PLCSIM and ARSim is also discussed for fully leveraging new features emerging from increased information and operational technology convergence witnessed in realistic, plant-level, environments. Qualitative and quantitative evaluation in comparison to the physical system while leveraging the resources provided by the cloud environment is carried out.

This paper presents a context-based filter to denoise grayscale images affected by random valued impulse noise. A support vector machine classifier is used for noise detection and two Markov filter variants are evaluated for their denoising capacity. The classifier needs to be trained on a set of training images. The experiments performed on another set of test images have shown that the support vector machine with the radial basis function kernel combined with the Markov+ filter is the best configuration, providing the highest noise detection accuracy. Our filter was compared with existing denoising methods, it being better on some images and comparable with them on others.

Colour is used increasingly often as quantitative indicator of combined continuous features such as temperature and humidity on geographical maps, or deformity on two axes in medical diagnosis. As such, generating flexible bivariate legends with particularly tough colour constraints for specific combinations of the variables has come to the forefront. The paper presents a systematic method of generating bivariate legends by first selecting a convenient colour subspace and then adjusting it through image processing techniques. The proposed method can generate a wide variety of legends with smart choices of parameters but remains simple enough to be worthy of consideration by specialists (in various fields) whose representations may benefit by becoming better intelligible. These legends can be used for pseudo-colouring of maps, diagrams, and many other images and graphical representations. An example is also provided for a comprehensive colouring to be used in the medical diagnosis of spine deformity.