Key Maker Algorithm: A Novel Human-Based Metaheuristic for Constrained Optimization

Abstract

In this paper, the Key Maker Algorithm (KMA) is introduced as a novel population-based metaheuristic inspired by the real-world process of locksmithing. The algorithm simulates the behaviour of a professional locksmith confronted with an unknown lock by employing two phases: exploration and exploitation. In the exploration phase, a combination of stochastic modifications guided by the best-known solutions generates a diverse population of candidate keys, reflecting the locksmith’s trial-and-error approach using approximate templates. In the exploitation phase, precise local refinements are applied to promising candidates to gradually approach the optimal key pattern. The algorithm’s mathematical formulation, based on adaptive update rules, ensures a dynamic balance between global search and local optimization. The computational complexity of the algorithm is comparable to mainstream population-based metaheuristics and is scalable. Extensive experimental results on the CEC 2011 benchmark suite, consisting of 22 constrained optimization problems, demonstrate that the KMA outperforms nine well-known metaheuristic algorithms in both performance and stability. Statistical analysis and boxplot visualizations indicate that the algorithm has high accuracy and reliability in solving diverse problems. These findings establish the KMA as an effective and dependable tool for tackling complex engineering optimization challenges.