Evolutionary algorithms and metaheuristics are computational intelligence tools and techniques used as global optimizers due to their population based characteristics without requiring any requisite to the objective/fitness evaluation function (e.g, continuity, derivability, and convexity). They are also not limited by the appearance of discrete and/or mixed variables, nor by the requirement of uncertainty quantification in the search process (useful in reliability based optimization and robust design). The possibility of dealing with more than one objective function has been possible through the evolutionary multiobjective optimization algorithms. This set of advantages, accompanied by the improved performance of computers, is fostering their increased use in research and industry in a wide variety of engineering branches. These methodologies are enabling the improvement in engineering design and optimization in areas, where the classical optimization techniques were not able to deal with real engineering problems, where the aforementioned requisites and limitations are common. For example, that is the case of computational engineering applications (e.g, automotive industry, aeronautic and aerospace industry, and civil, structural, and mechanical engineering), where the objective function/s values require the resolution of numerical modeling based on finite elements, boundary elements, finite volumes, and so on.

The potential advances in the use of evolutionary algorithms and metaheuristics in engineering applications bring an opportunity and also a challenge for researchers to improve and advance in design and optimization of products, systems, and services for societal benefit. The purpose of this special issue is to publish high-quality research articles as well as reviews that seek to address recent development from a variety of engineering fields relating to the application of evolutionary algorithms and metaheuristics for design and optimization and that will stimulate the continuing efforts to improve the current state of the art on the aforementioned field.

The proposal and application of recent and new algorithms (belonging to paradigms, e.g., genetic algorithms, evolution strategies, differential evolution, and particle swarm) as well as focus on development aspects, such as including surrogate modeling, parallelization, game theory, and hybridization, are encouraged. Advances of these methods for engineering applications are welcomed, both for single-objective and multiobjective optimization problems.

Potential topics include but are not limited to the following:

• Aerospace and aeronautical engineering
• Biomedical engineering
• Chemical engineering
• Civil engineering
• Electrical engineering
• Electronics engineering
• Industrial engineering
• Mechanical engineering
• Naval architecture
• Reliability engineering
• Structural engineering
• Telecommunications engineering