Nature-Inspired Algorithms and Applied Optimization [electronic resource] /
edited by Xin-She Yang.
- 1st ed. 2018.
- XI, 330 p. 42 illus., 28 illus. in color. online resource.
- Studies in Computational Intelligence, 744 1860-9503 ; .
- Studies in Computational Intelligence, 744 .
Mathematical Analysis of Nature-Inspired Algorithms -- A Review of No Free Lunch Theorems, and their Implications for Metaheuristic Optimisation -- Global Convergence Analysis of Cuckoo Search Using Markov Theory -- On Effeciently Solving the Vehicle Routing Problem with Time Windows Using the Bat Algorithm -- Variants of the Flower Pollination Algorithm: A Review.
This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.