Metaheuristic Optimization for Complex Engineering Design: A

Comprehensive Review of Structural and Mechanical Challenges

Nima Khodadadi1,* Aria Rabet 2

1 United Kingdom Liverpool Logistics Offshore and Marine (LOOM) Research Institute Liverpool John Moores University, Liverpool,

2 Department of Biology, College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA

Received: February 05, 2026 Revised: April 02, 2026 Accepted: June 04, 2026 ⋆ Corresponding author

ABSTRACT

Metaheuristic Optimization in Engineering has gained much attention recently because of its application in solving

challenging problems and nonlinear and constrained design often encountered in structural and mechanical design.

These optimization techniques are derived from natural phenomena, including Bio-evolution, Animal instincts and

the physical world, necessitating efficient and inexpensive design for engineers. In conventional design processes, the

design process may be tiresome and often unable to cope with large and complex engineering endeavors; however,

metaheuristic algorithms exhibit high effectiveness and functionality in optimizing designs in various sectors about

reinforced concrete structures and steel reinforced frames, mechanical parts, among others. This literature review

explains the current metaheuristic algorithms and their applicability to solving engineering problems, particularly

regarding computational time, quality and physical solution constraints. Difficulties regarding mechanical properties,

structural, and dynamic performances can effectively be resolved by utilizing metaheuristic algorithms such as

harmony search, teaching-learning-based optimization and other useful hybrid strategies to elevate the engineering

optimization field to another level. It also emphasizes CI application in improving the design processes and offers

clues on the future application of both the hybrid and the multi-objective optimization strategies in engineering.

Keywords: Metaheuristic Optimization Engineering Design Computational Efficiency Hybrid Algorithms Structural

Optimization

1. INTRODUCTION

Metaheuristic optimization has started to receive much attention

in the recent past as an ideal technique for solving

complex, real-world engineering problems. Many classical

methods, such as linear programming or gradient-based

approaches, do not scale well due to their assumptions of

convexity and smoothness of the objective and constraints,

and they can get trapped in local optima. On the other hand,

metaheuristic optimization algorithms can provide a more

flexible and more robust approach to the large and multidimensional

search space. They can reach near optimality

solutions even in the presence of highly nonlinear, noisy, or

constrained environments. This makes metaheuristics very

suitable in a wide range of fields, such as static, structural, and

mechanical engineering, since the problems encountered are

difficult to handle optimally using conventional optimization

techniques [1].

There has been much focus in the area of applying metaheuristic

optimization in prediction models. For example, Long

Short-Term Memory (LSTM) networks, which are widely

used for time-series forecasting, have been enhanced using