4. Advances in Music-Inspired Optimization Algorithms

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.