# Advances in Music-Inspired Optimization Algorithms

## Abstract

This chapter complements Chap. 3 by providing multiple innovative versions of the modern music-inspired optimization algorithms. First, the authors propose an innovative continuous/discrete TMS-MSA by borrowing the basic principles of the original continuous TMS-MSA in order to deal with the complicated, real-world, large-scale, non-convex, non-smooth optimization problems with a simultaneous combination of the continuous and discrete decision-making variables. Then, an innovative improved version of the proposed continuous/discrete TMS-MSA, called a two-stage computational multidimensional single-homogeneous enhanced melody search algorithm (TMS-EMSA), is developed in order to increase the efficiency and efficacy of the performance of this optimization algorithm. Moreover, an innovative version of the architecture of the proposed TMS-EMSA—a multi-stage computational multidimensional multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), multi-stage computational multidimensional single-inhomogeneous enhanced melody search algorithm (MMS-EMSA), or symphony orchestra search algorithm (SOSA)—is rigorously developed in order to appreciably enhance its performance, flexibility, robustness, and parallel capability. The newly developed SOSA has a multi-stage computational multidimensional and multiple-homogeneous or multi-stage computational multidimensional and single-inhomogeneous structure. Eventually, the chapter ends with the presentation of new multi-objective strategies for remodeling the architecture of the meta-heuristic music-inspired optimization algorithms.

## Keywords

Multi-stage computational multidimensional multiple-homogeneous enhanced melody search algorithm (MMM-EMSA) Continuous TMS-MSA Continuous/discrete TMS-MSA Multi-stage computational multidimensional single-inhomogeneous enhanced melody search algorithm (MMS-EMSA) Symphony orchestra search algorithm (SOSA) Two-stage computational multidimensional single-homogeneous enhanced melody search algorithm (TMS-EMSA)## References

- 1.Z.W. Geem, G.H. Kim, G.V. Loganathan, A new heuristic optimization algorithm: harmony search. Simulation
**76**(2), 60–68 (2001)CrossRefGoogle Scholar - 2.S.M. Ashrafi, A.B. Dariane, A novel and effective algorithm for numerical optimization: melody search (MS), in
*11th International Conference on Hybrid Intelligence Systems (HIS)*, 2011Google Scholar - 3.N. Rimsky-Korsakov,
*Principles of Orchestration: With Musical Examples Drawn from His Own Works*(Kalmus, New York, 1912)Google Scholar - 4.K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput.
**6**(2), 182–197 (2002)CrossRefGoogle Scholar - 5.M. Mahdavi, M. Fesanghary, E. Damangir, An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput.
**188**(2), 1567–1579 (2007)MathSciNetzbMATHGoogle Scholar