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چکیده
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The field of global optimization has garnered significant attention due to its critical applications across various domains, including engineering, finance, and artificial intelligence. Global optimization seeks to identify the best possible solution from a set of feasible solutions, particularly in scenarios where the objective function exhibits non-convex characteristics with multiple local minima. Traditional optimization techniques often fall short in these complex landscapes, necessitating the development of innovative algorithms that can effectively navigate such challenges. This paper presents a new version of a single candidate optimizer designed to enhance the efficiency and effectiveness of global optimization processes, contributing to the growing body of research in this area [1].
The proposed single candidate optimizer (SCO) is grounded in the principles of evolutionary algorithms but distinguishes itself by relying solely on a single candidate solution throughout the optimization process. This approach allows for a more focused exploration of the solution space, reducing computational overhead while maintaining robust performance. By concentrating on a single candidate, the algorithm can iteratively refine its search based on previous evaluations, thus improving convergence rates and solution quality compared to traditional multi-candidate methods [2].
One of the key innovations of this new optimizer is its integration with predictive modeling techniques, which enhance decision-making during the search process. By employing surrogate models that predict potential performance outcomes based on historical data, the optimizer can strategically explore promising regions of the solution space while avoiding less fruitful areas. This method not only accelerates convergence but also minimizes the number of costly function evaluations required, making it particularly advantageous in scenarios where evaluations are expensive or time-consuming [3].
Furthermore,
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