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Many metaheuristic algorithms are created by taking inspiration from nature, society, art, or human behavior. However, sometimes these ideas look new only because of the story behind them. If the mapping between the story and the mathematical search process is not clear, measurable, and repeatable, the method may be misleading. In this paper, based on basic principles of stochastic search, population behavior, and standard benchmark testing, we do two things. First, we propose a general guideline that shows how a metaheuristic algorithm can be systematically built from a poem stanza or from the known traits of a famous person. Second, we introduce a new algorithm called Shakespearean Soliloquy Optimization (SSO). This method is inspired by dramatic elements in Shakespeare’s works such as soliloquy (selfreflection), rhythmic movement, chorus interaction, and the conflict–resolution story arc. We test SSO on a simple quadratic function 𝑧 = (𝑥 − 5)2 + (𝑦 − 4)2and compare its performance with PSO, ABC, and a continuous version of MBO. Then we evaluate it on five well-known benchmark functions: Sphere, Rosenbrock, Rastrigin, Ackley, and Griewank.