A MODIFIED HARRIS HAWKS OPTIMIZATION ALGORITHM FOR SOLVING CONSTRAINED ENGINEERING OPTIMIZATION PROBLEMS
Metaheuristics optimization has gained popularity in recent years for its effectiveness in solving real-world problems, such as engineering design. These techniques are especially helpful in solving nonlinear, non-convex, non-differentiable, high-dimensional, NP-hard, and discrete search space problems that are difficult to solve with traditional optimization techniques. In this study, a modified Harris Hawks Optimization (MHHO) algorithm is proposed using a mutation-selection strategy and crossover operator to global optimization problems. It can control the balance between exploration and exploitation in the search process. This flexibility allows the algorithm to adapt to different optimization problems and search landscapes, potentially improving its performance in finding optimal or near-optimal solutions. The proposed method has been tested on a variety of constrained structural engineering design problems and compared with well-known metaheuristic algorithms. The results from systematic experiments demonstrated that the MHHO algorithm provided more reliable solutions than other well-known algorithms. Furthermore, the experimental findings show that MHHO outperformed other metaheuristic algorithms in terms of optimization performance.
Optimization, Meta-heuristics, Structural Engineering Design, Harris Hawks Algorithm, Global Optimization Problems.