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.

Generating multidimensional rugged landscapes is very important for testing and benchmarking, considering the increasing number of multi-objective optimization problems and the pervasive use of computational systems. The diverse nature of such problems has led to a variety of algorithmic solutions, designed to create more scalable and problem-independent algorithms. This paper introduces a method for constructing a class of discrete multidimensional Thomae-like functions, generalizing the classical Thomae function. While the original function is continuous at irrational points and discontinuous at rational ones, we extend this approach to higher-dimensional discrete spaces. We present the properties of three known analytic generalizations and analyze five proposed discrete cases according to the relationship between the numbers of input and output values. The most important properties of such functions are discussed, including their periodicity, number of local maxima, autocorrelation, and output variations under small input changes. The last property has significant importance for simulating and testing in various application areas. This work unifies and extends existing concepts into a systematic method for generating discrete multidimensional functions, opening new directions for both theoretical investigations and practical applications of algorithms in engineering.