This paper introduces and investigates an enhanced partial reinforcement optimization algorithm (E-PROA), a novel evolutionary algorithm inspired by partialreinforcement theory to efficiently solve complex engineerin...
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This paper introduces and investigates an enhanced partial reinforcement optimization algorithm (E-PROA), a novel evolutionary algorithm inspired by partialreinforcement theory to efficiently solve complex engineering optimization problems. The proposed algorithm combines the partial reinforcement optimization algorithm (PROA) with a quasi-oppositional learning approach to improve the performance of the pure PROA. The E-PROA was applied to five distinct engineering design components: speed reducer design, step-cone pulley weight optimization, economic optimization of cantilever beams, coupling with bolted rim optimization, and vehicle suspension arm optimization problems. An artificial neural network as a metamodeling approach is used to obtain equations for shape optimization. Comparative analyses with other benchmark algorithms, such as the ship rescue optimizationalgorithm, mountain gazelle optimizer, and cheetah optimizationalgorithm, demonstrated the superior performance of E-PROA in terms of convergence rate, solution quality, and computational efficiency. The results indicate that E-PROA holds excellent promise as a technique for addressing complex engineering optimization problems.
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