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The Monarchy Driven Optimization technique

We present a novel human society inspired algorithm for solving single-objective bound constrained optimization problems. The proposed Monarchy Driven Optimization (MDO) algorithm is a population-based iterative global optimization technique for multi-dimensional and multi-modal problems. At its core, this technique introduces a monarchial society where the outlook of its population is fashioned by the thoughts of individuals and the monarch. A detailed study including the tuning of MDO parameters is presented along with the theory. It is applied to standard benchmark functions comprising unimodal and multi-modal as well as rotated functions. The results section suggests that, in most instances, MDO outperforms other well-known techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Gravitational Search Algorithm (GSA), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Artificial Bee Colony (ABC) optimization in terms of final convergence value and mean convergence value, thus proves to be a robust optimization technique.