Modern Music-Inspired Optimization Algorithms for Electric Power Systems: Modeling, Analysis and Practice

Description

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. Front Matter ....Pages i-xxvii Front Matter ....Pages 1-1 Introduction to Meta-heuristic Optimization Algorithms (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 3-20 Introduction to Multi-objective Optimization and Decision-Making Analysis (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 21-45 Music-Inspired Optimization Algorithms: From Past to Present (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 47-95 Advances in Music-Inspired Optimization Algorithms (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 97-262 Front Matter ....Pages 263-263 Power Systems Operation (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 265-325 Power Systems Planning (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 327-625 Power Filters Planning (Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier)....Pages 627-716 Back Matter ....Pages 717-727