Nature-Inspired Optimization Algorithms in Engineering: Overview and Applications

Nature-inspired computation has become popular in engineering applications and nature-inspired algorithms tend to be simple and flexible and yet sufficiently efficient to deal with highly nonlinear optimization problems. In this chapter, we first review the brief history of nature-inspired optimization algorithms, followed by the introduction of a few recent algorithms based on swarm intelligence. Then, we analyze the key characteristics of optimization algorithms and discuss the choice of algorithms. Finally, some case studies in engineering are briefly presented.

[1]  Abdul Hanan Abdullah,et al.  Scheduling jobs on grid computing using firefly algorithm , 2011 .

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

[3]  V. Mani,et al.  Clustering using firefly algorithm: Performance study , 2011, Swarm Evol. Comput..

[4]  Simon Fong,et al.  Feature Selection in Life Science Classification: Metaheuristic Swarm Search , 2014, IT Professional.

[5]  Xin-She Yang,et al.  Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan , 2014, Appl. Soft Comput..

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

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

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

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

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

[11]  Xin-She Yang,et al.  Optimal test sequence generation using firefly algorithm , 2013, Swarm Evol. Comput..

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Simon Fong,et al.  A Novel Hybrid Self-Adaptive Bat Algorithm , 2014, TheScientificWorldJournal.

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

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

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

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

[18]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[19]  Xin-She Yang,et al.  Discrete cuckoo search algorithm for the travelling salesman problem , 2014, Neural Computing and Applications.

[20]  Xin-She Yang,et al.  Color Image Segmentation By Cuckoo Search , 2015, Intell. Autom. Soft Comput..

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

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

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

[24]  Jon Atli Benediktsson,et al.  Automatic registration of multi-temporal remote sensing images based on nature-inspired techniques , 2014 .

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

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

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

[28]  Janez Brest,et al.  Towards the Novel Reasoning among Particles in PSO by the Use of RDF and SPARQL , 2014, TheScientificWorldJournal.

[29]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[30]  Xin-She Yang,et al.  Recent Advances in Swarm Intelligence and Evolutionary Computation , 2015, Recent Advances in Swarm Intelligence and Evolutionary Computation.

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

[32]  Xin-She Yang,et al.  Sizing optimization of truss structures using flower pollination algorithm , 2015, Appl. Soft Comput..

[33]  Craig A. Tovey,et al.  On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers , 2004, Adapt. Behav..

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

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

[36]  David H. Wolpert,et al.  Coevolutionary free lunches , 2005, IEEE Transactions on Evolutionary Computation.

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

[38]  Kevin M. Passino,et al.  Bacterial Foraging Optimization , 2010, Int. J. Swarm Intell. Res..

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

[40]  Xin-She Yang,et al.  Random-key cuckoo search for the travelling salesman problem , 2015, Soft Comput..

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

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

[43]  D. M. Hutton,et al.  The Essential Turing , 2007 .

[44]  Iztok Fister,et al.  Firefly Algorithm: A Brief Review of the Expanding Literature , 2014 .

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

[46]  A. Schrijver On the History of Combinatorial Optimization (Till 1960) , 2005 .