Metaheuristic Optimization: Nature-Inspired Algorithms and Applications

Turing’s pioneer work in heuristic search has inspired many generations of research in heuristic algorithms. In the last two decades, metaheuristic algorithms have attracted strong attention in scientific communities with significant developments, especially in areas concerning swarm intelligence based algorithms. In this work, we will briefly review some of the important achievements in metaheuristics, and we will also discuss key implications in applications and topics for further research.

[1]  Xin-She Yang,et al.  Two-stage eagle strategy with differential evolution , 2012, Int. J. Bio Inspired Comput..

[2]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[3]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[4]  Kathleen Steinhöfel,et al.  Stochastic Algorithms: Foundations and Applications , 2001, Lecture Notes in Computer Science.

[5]  Z. Geem.  Music-Inspired Harmony Search Algorithm: Theory and Applications , 2009 .

[6]  Joshua D. Knowles,et al.  Some multiobjective optimizers are better than others , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[7]  Carsten Witt,et al.  Bioinspired Computation in Combinatorial Optimization , 2010, Bioinspired Computation in Combinatorial Optimization.

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

[9]  James A. R. Marshall,et al.  Beyond No Free Lunch: Realistic algorithms for arbitrary problem classes , 2009, IEEE Congress on Evolutionary Computation.

[10]  Frank Neumann,et al.  Bioinspired computation in combinatorial optimization: algorithms and their computational complexity , 2010, GECCO '12.

[11]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[12]  Kenneth Morgan,et al.  Modified cuckoo search: A new gradient free optimisation algorithm , 2011 .

[13]  Carlos A. Coello Coello,et al.  Asymptotic convergence of metaheuristics for multiobjective optimization problems , 2006, Soft Comput..

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

[15]  Anne Auger,et al.  Theory of Randomized Search Heuristics: Foundations and Recent Developments , 2011, Theory of Randomized Search Heuristics.

[16]  L. D. Whitley,et al.  The No Free Lunch and problem description length , 2001 .

[17]  Panos M. Pardalos,et al.  Encyclopedia of Optimization , 2006 .

[18]  José R. Álvarez,et al.  Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach: First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, June 15-18, 2005, Proceedings, Part II , 2005, IWINAC.

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

[20]  Amir Hossein Gandomi,et al.  Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect , 2012, Appl. Soft Comput..

[21]  Steffen Christensen,et al.  What can we learn from No Free Lunch? a first attempt to characterize the concept of a searchable function , 2001 .

[22]  G. Calafiore,et al.  Probabilistic and Randomized Methods for Design under Uncertainty , 2006 .

[23]  Jack Copeland.  Interview with Jack Copeland, Professor of Philosophy at the University of Canterbury, New Zealand, and Director of the Turing Archive for the History of Computing , 2014 .

[24]  İsmail Durgun,et al.  Structural Design Optimization of Vehicle Components Using Cuckoo Search Algorithm , 2012 .

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

[26]  Kin Keung Lai,et al.  TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS: SELECTING OR COMBINING? , 2005 .

[27]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[28]  Marc Toussaint,et al.  On Classes of Functions for which No Free Lunch Results Hold , 2001, Inf. Process. Lett..

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

[30]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

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

[32]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[33]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010, Int. J. Math. Model. Numer. Optimisation.

[34]  Seppo J. Ovaska,et al.  A general framework for statistical performance comparison of evolutionary computation algorithms , 2006, Inf. Sci..

[35]  Xin-She Yang,et al.  Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms , 2005, IWINAC.

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

[37]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[38]  Olivier Teytaud,et al.  Continuous Lunches Are Free Plus the Design of Optimal Optimization Algorithms , 2010, Algorithmica.

[39]  O. Bhabha.  The Essential Turing , 2011 .

[40]  Tom Fearn,et al.  Particle Swarm Optimisation , 2014 .

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

[42]  Amir Hossein Gandomi,et al.  Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization , 2012, Comput. Math. Appl..

[43]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[44]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[45]  Barry J. Adams,et al.  Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation , 2007, J. Frankl. Inst..

[46]  Zong Woo Geem,et al.  Music-Inspired Harmony Search Algorithm , 2009 .

[47]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

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

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

[50]  Germán Terrazas,et al.  Nature Inspired Cooperative Strategies for Optimization, NICSO 2010, May 12-14, 2010, Granada, Spain , 2012, NISCO.

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

[52]  J. Spall,et al.  Theoretical framework for comparing several popular stochastic optimization approaches , 2002 .

[53]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

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

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

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

[57]  Walter J. Gutjahr,et al.  Convergence Analysis of Metaheuristics , 2010, Matheuristics.