A Comparative Analysis of the Applicability of New Generation Metaheuristic Algorithms to Engineering Optimization Problems

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Abstract
The quest to solve complex, multidimensional, and nonlinear optimization problems frequently encountered in engineering and scientific research has increased interest in heuristic and metaheuristic algorithms. In this study, six new-generation metaheuristic algorithms that have not been previously applied to engineering problems in the literature are introduced and tested on various engineering problems. The Hiking Optimization Algorithm (HOA) models route selection based on slope, simulating the paths followed by individuals during nature hikes. The Fungal Growth Optimization (FGO) algorithm is inspired by the growth and propagation mechanisms of fungi, while the Mirage Simulation Optimization (MSO) seeks solutions based on the exploration-exploitation balance of atmospheric mirages. The Stellar Oscillation Optimization (SOO) mimics the internal oscillations of stars and follows a strategy from global to local search. The mathematically grounded Adaptive Evolutionary Algorithm (AEA) offers an evolutionary learning approach by evaluating potential knowledge in unexplored regions. These algorithms were tested for the first time on six engineering design problems, including a cantilever beam, speed reducer, crashworthiness, multi-disc clutch-brake, gas transmission compressor, and industrial cooling system. The results revealed that the Stellar Oscillation Optimization (SOO) algorithm demonstrated superior performance in terms of average solution quality and convergence speed compared to the other algorithms.

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