Dear Colleagues,

At present, many engineering optimization problems cannot be solved by traditional methods based on gradient. Several reasons make traditional methods unsuitable for complex engineering problems, such as usage of derivatives that are not available in simulation-based systems, where a mathematical formulation is difficult, and also poor performance in nonconvex landscapes, where local minimum/maximum can stop the optimization algorithm. In the last decade, new meta-heuristic algorithms have arisen, such as Firefly Algorithm, Harmonic Search, and Bat Optimization, among other approaches, that present significant performances in many engineering areas, such as telecommunications, robotics, mechanical design, and power systems, among others. Furthermore, multiobjective approaches like the ones based on Pareto dominance are appropriate for engineering applications, where normally, efficiency and cost performance metrics are counterbalanced.

This Special Issue pursues both novel metaheuristic algorithms and the application of existing metaheuristic approaches in engineering problems. Since abundant liteturate can be found in some engineering areas, both surveys and literature reviews are welcome.

The possible topics of interest include but are not limited to the following areas:

• Genetic Algorithm (GA) for engineering optimization problems;
• Swarm Optimization Algorithms (PSO, Firefly, Ant Colony, etc.) for engineering optimization problems;
• Bio-inspired optimization algorithms for engineering optimization problems;
• Genetic programing for engineering optimization problems;
• Evolutionary strategies for engineering optimization problems;
• Multiobjective optimization for engineering optimization problems;
• Evolutionary algorithms based on subrogate models for engineering optimization problems;
• Hybrid metaheuristic algorithms for engineering optimization problems;
• Parallel metaheuristic algorithms for engineering optimization problems;
• Combination of machine learning approaches and metaheuristic algorithms for engineering optimization problems;
• Application of metaheuristic algorithms for adjusting the hyperparameter of Deep Learning models applied to engineering problems.

Dr. Daniel Gutiérrez Reina
Dr. Kathiravan Srinivasan
Dr. vishal sharma
Guest Editor

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• Evolutionary computation
• Bio-inspired optimization
• Swarm optimization
• Machine learning
• Genetic programming