Swarm Intelligence and Evolutionary Computation: Overview and Analysis

In many applications, the complexity and nonlinearity of the problems require novel and alternative approaches to problem solving. In recent years, nature-inspired algorithms, especially those based on swarm intelligence, have become popular, due to the simplicity and flexibility of such algorithms. Here, we review briefly some recent algorithms and then outline the self-tuning framework for parameter tuning. We also discuss some convergence properties of the cuckoo search and the bat algorithm. Finally, we present some open problems as further research topics.

[1]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[2]  Xin-She Yang,et al.  Bat algorithm for multi-objective optimisation , 2011, Int. J. Bio Inspired Comput..

[3]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[4]  A. E. Eiben,et al.  Parameter tuning for configuring and analyzing evolutionary algorithms , 2011, Swarm Evol. Comput..

[5]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[6]  Xin-She Yang,et al.  Flower pollination algorithm: A novel approach for multiobjective optimization , 2014, ArXiv.

[7]  Xin-She Yang,et al.  A framework for self-tuning optimization algorithm , 2013, Neural Computing and Applications.

[8]  Xin-She Yang,et al.  Chapter 10 – Bat Algorithms , 2014 .

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  Xin-She Yang,et al.  Cuckoo Search and Firefly Algorithm , 2014 .

[11]  Ilya Pavlyukevich.  Lévy flights, non-local search and simulated annealing , 2007, J. Comput. Phys..

[12]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[13]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[14]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[15]  Simon Fong,et al.  Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications , 2011, NDT.

[16]  Xin-She Yang,et al.  Multi-Objective Flower Algorithm for Optimization , 2014, ICCS.

[17]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[18]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[19]  L. Booker.  Perspectives on adaptation in natural and artificial systems , 2004 .

[20]  Iztok Fister,et al.  Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm , 2013, Comput. Optim. Appl..

[21]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[22]  Xin-She Yang,et al.  Cuckoo Search and Firefly Algorithm: Theory and Applications , 2013 .

[23]  Xin-She Yang,et al.  Multiobjective cuckoo search for design optimization , 2013, Comput. Oper. Res..

[24]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[25]  LU Qiu-qin.  Bat algorithm with global convergence for solving large-scale optimization problem , 2013 .

[26]  Janez Brest,et al.  Modified firefly algorithm using quaternion representation , 2013, Expert Syst. Appl..

[27]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[28]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[29]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[30]  Iztok Fister,et al.  A hybrid bat algorithm , 2013, ArXiv.

[31]  R. Storn,et al.  On the usage of differential evolution for function optimization , 1996, Proceedings of North American Fuzzy Information Processing.

[32]  Yang Song-ming,et al.  Markov Model and Convergence Analysis Based on Cuckoo Search Algorithm , 2012 .

[33]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[34]  Roman V. Belavkin,et al.  Optimal measures and Markov transition kernels , 2010, J. Glob. Optim..

[35]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.