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Abstract The field of global optimization faces the persistent challenge of developing metaheuristics that are both highly effective and computationally efficient. Motivated by the limitations of many nature-inspired algorithms, which are often susceptible to premature convergence, this research explores formal intellectual processes to create a more robust search mechanism. We propose and formalize the Dialectical Search (DS) framework, a metaheuristic inspired by the philosophical principle of Thesis, Antithesis, and Synthesis. The framework’s efficacy was validated in a comprehensive empirical study where two primary variants, DS-Original and DS-Hybrid, were benchmarked against a diverse suite of seven other classical and state-of-the-art algorithms on multiple real-world datasets. The benchmark revealed that the DS-Original variant consistently achieves a solution quality that is statistically indistinguishable from top-performing algorithms; for instance, on the Breast Cancer Wisconsin dataset, it achieved a cross-validation error of 0.02415, matching the performance of far slower methods. Critically, it delivers this state-of-the-art performance with exceptional computational efficiency, executing approximately 20% faster than the Genetic Algorithm and an order of magnitude faster—48.14 s versus 481.25 s—than the Firefly Algorithm. Furthermore, the simpler DS-Original’s superior practical performance over its more complex hybrid counterpart provides a valuable insight into the No Free Lunch (NFL) theorem. Therefore, we conclude that the Dialectical Search framework, particularly the DS-Original variant, represents a significant contribution to the field, offering a validated and highly advantageous balance of solution accuracy and computational efficiency for solving complex optimization problems.
Hasan et al. (Thu,) studied this question.