In today's world with an increase in the breadth and scope of real-world engineering
optimization problems as well as with the advent of big data improving the performance and
efficiency of algorithms for solving such problems has become an indispensable need for
specialists and researchers. In contrast to conventional books in the field that employ
traditional single-stage computational single-dimensional and single-homogeneous optimization
algorithms this book addresses multiple newfound architectures for meta-heuristic
music-inspired optimization algorithms. These proposed algorithms with multi-stage
computational multi-dimensional and multi-inhomogeneous structures bring about a new
direction in the architecture of meta-heuristic algorithms for solving complicated real-world
large-scale non-convex non-smooth engineering optimization problems having a non-linear
mixed-integer nature with big data. The architectures of these new algorithms may also be
appropriate for finding an optimal solution or a Pareto-optimal solution set with higher
accuracy and speed in comparison to other optimization algorithms when feasible regions of the
solution space and or dimensions of the optimization problem increase. This book unlike
conventional books on power systems problems that only consider simple and impractical models
deals with complicated techno-economic real-world large-scale models of power systems
operation and planning. Innovative applicable ideas in these models make this book a precious
resource for specialists and researchers with a background in power systems operation and
planning.Provides an understanding of the optimization problems and algorithms particularly
meta-heuristic optimization algorithms found in fields such as engineering economics
management and operations research Enhances existing architectures and develops innovative
architectures for meta-heuristic music-inspired optimization algorithms in order to deal with
complicated real-world large-scale non-convex non-smooth engineering optimization problems
having a non-linear mixed-integer nature with big data Addresses innovative multi-level
techno-economic real-world large-scale computational-logical frameworks for power systems
operation and planning and illustrates practical training on implementation of the frameworks
using the meta-heuristic music-inspired optimization algorithms.
