Scope

Deadline Date: 15 March 2026

Optimization in engineering is a key contribution to enhancing the functionality, efficiency, and dependability of systems in a wide range of domains including structural mechanics, energy systems, fluid dynamics, and control processes. Most of these optimization problems are nonlinear, strongly constrained, or formulated over-complicated solution spaces that render it hard to solve such problems with the use of conventional numerical approaches alone. To overcome such limitations, metaheuristic frameworks have attracted much consideration due to their flexibility and problem-solving ability. Such frameworks do not depend on gradient information or rigid mathematical formulations that enable them to search complex and large spaces with flexibility. The iterative nature encourages exploration and exploitation and results in feasible solutions even if exact methods are not feasible. More researchers are combining metaheuristics with well-known numerical methods to form hybrid schemes with improved solution accuracy and reliability. These developments are pushing the scope of problems that can be handled successfully in engineering. The increasing applicability of these techniques highlights the necessity for ongoing investigation of their combinations with computational modeling and simulation methods.

Despite their strengths, applying metaheuristic algorithms to engineering problems presents several challenges. The performance of these algorithms depends heavily on the nature of the problem and selecting the most suitable strategy often requires expert knowledge. Parameter tuning, maintaining search diversity, and avoiding premature convergence remain ongoing concerns. Also, when metaheuristics are used in conjunction with computationally intensive high-fidelity numerical simulations such as iterative solvers or detailed models computational expenses may become quite high. In safety-critical or real-time environments predictable non-convergence and instability of measuring solution quality may also constrain their application. Also, inconsistency in benchmarking throughout research prevents objective algorithm performance comparison. These challenges invite more systematic frameworks, adaptive algorithmic designs, and increased validation practice emphasis. Future research will address more efficient hybrid models, increased interpretability, and the exploitation of advances in parallel computing to scale up metaheuristic optimization and make it more reliable within engineering applications.

We thus suggest a targeted set of research to further the convergence of metaheuristic platforms with numerical techniques in engineering optimization. This special issue will feature empirical, theoretical, and computational results that solve practical engineering problems. Interdisciplinary insights are invited on novel algorithmic formulations, combined approaches, and verification techniques to enhance the scalability and robustness of engineering solutions.

Potential topics include but are not limited to the following:

• Hybrid Metaheuristic-Numerical Frameworks for Engineering Optimization.
• Scalable Metaheuristic Algorithms in Simulation-Driven Engineering Design.
• Empirical Evaluation of Metaheuristic Methods in Complex Engineering Applications.
• Adaptive Parameter Control in Metaheuristic Engineering Systems.
• Parallel and Distributed Metaheuristics for Large-Scale Engineering Optimization.
• Uncertainty-Aware Metaheuristic Approaches for Engineering Simulations.
• Integration of Metaheuristics with Finite Element Analysis in Mechanical Design.
• Interpretable Metaheuristic Models for Engineering Decision Support.
• Metaheuristic Strategies for Multi-Objective Engineering Design Problems.
• Next-Generation Metaheuristics for Cyber-Physical and Control Systems.
• Validation Frameworks for Metaheuristic-Based Engineering Optimization.
• Metaheuristic-Driven Innovation in Engineering Design Automation.
• Cross-Disciplinary Applications of Metaheuristics in Computational Engineering.