SEA 2011: Experimental Algorithms pp 21-32 | Cite as

Metaheuristic Optimization: Algorithm Analysis and Open Problems

  • Xin-She Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6630)

Abstract

Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majority of metaheuristic algorithms, while efficiency analysis is equally challenging. In this paper, we intend to provide an overview of convergence and efficiency studies of metaheuristics, and try to provide a framework for analyzing metaheuristics in terms of convergence and efficiency. This can form a basis for analyzing other algorithms. We also outline some open questions as further research topics.

Keywords

Markov Chain Particle Swarm Optimization Random Walk Simulated Annealing Convergence Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xin-She Yang
    • 1
  1. 1.Mathematics and Scientific ComputingNational Physical LaboratoryTeddingtonUK

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