Shakespearean Soliloquy Optimization (SSO): A Structured Metaheuristic Framework for Global Optimization

Author : S.Aynur Uysal, Mitat uysal

Abstract : This paper proposes a structured framework for designing concept-inspired metaheuristic algorithms. Although many metaheuristics are derived from nature, society, art, or human behavior, their apparent novelty may be superficial if the mapping to mathematical search operators is not clearly defined, measurable, and reproducible. 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 (self reflection), rhythmic movement, chorus interaction, and the conflict–resolution story arc. We test SSO on a simple quadratic function and 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.

Keywords : Metaheuristics, metaphor-to-algorithm mapping, Shakespearean soliloquy, swarm intelligence.

Conference Name : International Conference on Machine Learning and Artificial Intelligence Applications (ICMLAIA-26)

Conference Place : London, UK

Conference Date : 4th May 2026