Theoretical Analysis of Nature-Inspired Optimization Algorithms [PDF]

tackle such hard problems. In many cases, nature-inspired metaheuristic algorithms can be a good alternative and such al

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Theoretical Analysis of Nature-Inspired Optimization Algorithms (A tutorial at EANN 2018)

Tutor: Xin-She Yang, Middlesex University London, UK Many problems in optimization and computational intelligence are very challenging to solve, and some of these problems can be NP-hard, which means that there are often no efficient algorithms to tackle such hard problems. In many cases, nature-inspired metaheuristic algorithms can be a good alternative and such algorithms include genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search and many others. Over the last two decades, natureinspired optimization algorithms have become increasingly popular in solving large-scale, nonlinear, global optimization with many real-world applications. They also become an important of part of optimization and computational intelligence. This tutorial will provide a critical analysis of recent algorithms using mathematical theories such as Markov chains, dynamic systems, random walks and self-organization systems. This will provide some insight into these algorithms concerning their convergence rates and stability.

Topics and Format: This tutorial intends to introduce the fundamentals and latest advances of the state-of-the-art nature-inspired algorithms with the focus on mathematical analysis on new algorithms using a unified theoretical framework of Markov chain theory, random walks, dynamic systems and self-organization theory. Topics include     

Essence of an evolutionary algorithm Mathematical analysis of algorithms using Markov chains and self-organization Convergence and stability using dynamic systems and Markov chain theory Review of some recent theoretical results concerning evolutionary algorithms Introduction of selected case studies in applications with demo codes in Matlab

Tutor: Dr. Xin-She Yang School of Science and Technology, Middlesex University, London NW4 4BT, United Kingdom http://scholar.google.co.uk/citations?user=fA6aTlAAAAAJ

Email: [email protected]

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