A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems

[1]  Cyril Picard,et al.  Realistic Constrained Multiobjective Optimization Benchmark Problems From Design , 2021, IEEE Transactions on Evolutionary Computation.

[2]  Ruocheng Guo,et al.  A Survey of Learning Causality with Data , 2018, ACM Comput. Surv..

[3]  Marco A. Boschetti,et al.  Matheuristics , 2021, EURO Advanced Tutorials on Operational Research.

[4]  Hisao Ishibuchi,et al.  Proposal of a Realistic Many-Objective Test Suite , 2020, PPSN.

[5]  Guohua Wu,et al.  A test-suite of non-convex constrained optimization problems from the real-world and some baseline results , 2020, Swarm Evol. Comput..

[6]  Wei Chen,et al.  Paradoxes in Numerical Comparison of Optimization Algorithms , 2020, IEEE Transactions on Evolutionary Computation.

[7]  Javier Del Ser,et al.  Fairness in Bio-inspired Optimization Research: A Prescription of Methodological Guidelines for Comparing Meta-heuristics , 2020, ArXiv.

[8]  Yusuke Nojima,et al.  Towards realistic optimization benchmarks: a questionnaire on the properties of real-world problems , 2020, GECCO Companion.

[9]  Hisao Ishibuchi,et al.  An easy-to-use real-world multi-objective optimization problem suite , 2020, Appl. Soft Comput..

[10]  Xin Yao,et al.  A Survey of Automatic Parameter Tuning Methods for Metaheuristics , 2020, IEEE Transactions on Evolutionary Computation.

[11]  Ruocheng Guo,et al.  Causal Interpretability for Machine Learning - Problems, Methods and Evaluation , 2020, SIGKDD Explor..

[12]  S. García,et al.  Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations , 2020, Cognitive Computation.

[13]  Alejandro Barredo Arrieta,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2019, Inf. Fusion.

[14]  Handing Wang,et al.  A repository of real-world datasets for data-driven evolutionary multiobjective optimization , 2019, Complex & Intelligent Systems.

[15]  Tansel Dökeroglu,et al.  A survey on new generation metaheuristic algorithms , 2019, Comput. Ind. Eng..

[16]  Xin-She Yang,et al.  Bio-inspired computation: Where we stand and what's next , 2019, Swarm Evol. Comput..

[17]  Sean Luke,et al.  ECJ at 20: toward a general metaheuristics toolkit , 2019, GECCO.

[18]  Rafal Biedrzycki,et al.  On equivalence of algorithm's implementations: the CMA-ES algorithm and its five implementations , 2019, GECCO.

[19]  Marie-Eléonore Kessaci,et al.  Meta-learning on flowshop using fitness landscape analysis , 2019, GECCO.

[20]  Hisao Ishibuchi,et al.  A Scalable Multimodal Multiobjective Test Problem , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

[21]  Hugo Terashima-Marín,et al.  Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning , 2019, Expert Syst. Appl..

[22]  Javier Del Ser,et al.  jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics , 2019, Swarm Evol. Comput..

[23]  Guohua Wu,et al.  Ensemble strategies for population-based optimization algorithms - A survey , 2019, Swarm Evol. Comput..

[24]  Yang Lou,et al.  On constructing alternative benchmark suite for evolutionary algorithms , 2019, Swarm Evol. Comput..

[25]  Yongxi Huang,et al.  The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology , 2019, Eur. J. Oper. Res..

[26]  Heike Trautmann,et al.  Automated Algorithm Selection: Survey and Perspectives , 2018, Evolutionary Computation.

[27]  Peter R. Killeen,et al.  Predict, Control, and Replicate to Understand: How Statistics Can Foster the Fundamental Goals of Science , 2018, Perspectives on Behavior Science.

[28]  Rodolphe Le Riche,et al.  Global sensitivity analysis for optimization with variable selection , 2017, SIAM/ASA J. Uncertain. Quantification.

[29]  Marius Lindauer,et al.  Pitfalls and Best Practices in Algorithm Configuration , 2017, J. Artif. Intell. Res..

[30]  Yuhui Shi,et al.  Metaheuristic research: a comprehensive survey , 2018, Artificial Intelligence Review.

[31]  Jennifer A. Joy-Gaba,et al.  The Reproducibility Project: A Model of Large-Scale Collaboration for Empirical Research on Reproducibility , 2018, Implementing Reproducible Research.

[32]  Hans A. Jacobsen,et al.  PreDict , 2018, Proceedings of the 19th International Middleware Conference.

[33]  Javier J. Sánchez Medina,et al.  Multi-Objective Optimization of Bike Routes for Last-Mile Package Delivery with Drop-Offs , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[34]  Grega Vrbancic,et al.  NiaPy: Python microframework for building nature-inspired algorithms , 2018, J. Open Source Softw..

[35]  Liang Feng,et al.  Insights on Transfer Optimization: Because Experience is the Best Teacher , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[36]  Atharv Bhosekar,et al.  Advances in surrogate based modeling, feasibility analysis, and optimization: A review , 2018, Comput. Chem. Eng..

[37]  Eneko Osaba,et al.  Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems , 2018, Neurocomputing.

[38]  Alexander Herzog,et al.  On Time Optimization of Centroidal Momentum Dynamics , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[39]  Fred W. Glover,et al.  A History of Metaheuristics , 2015, Handbook of Heuristics.

[40]  Manuel Laguna,et al.  Tabu Search , 1997 .

[41]  Xin-She Yang,et al.  Nature-Inspired Algorithms and Applied Optimization , 2018 .

[42]  Xin-She Yang,et al.  Mathematical Analysis of Nature-Inspired Algorithms , 2018 .

[43]  Bruce McMillin,et al.  Software engineering: What is it? , 2018, 2018 IEEE Aerospace Conference.

[44]  Xin Yao,et al.  A benchmark test suite for evolutionary many-objective optimization , 2017 .

[45]  Francisco Herrera,et al.  Genetic and Memetic Algorithm with Diversity Equilibrium based on Greedy Diversification , 2017, ArXiv.

[46]  Jun Zhang,et al.  Benchmarking Stochastic Algorithms for Global Optimization Problems by Visualizing Confidence Intervals , 2017, IEEE Transactions on Cybernetics.

[47]  Ye Tian,et al.  PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] , 2017, IEEE Computational Intelligence Magazine.

[48]  Manuel Chica,et al.  Why Simheuristics? Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation , 2017, SSRN Electronic Journal.

[49]  Marco Zaffalon,et al.  Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis , 2016, J. Mach. Learn. Res..

[50]  Iain Dunning,et al.  JuMP: A Modeling Language for Mathematical Optimization , 2015, SIAM Rev..

[51]  Manuel Iori,et al.  Bin packing and cutting stock problems: Mathematical models and exact algorithms , 2016, Eur. J. Oper. Res..

[52]  André Carlos Ponce de Leon Ferreira de Carvalho,et al.  Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: A comparison of meta-features , 2016, Neurocomputing.

[53]  Dario Pacciarelli,et al.  An iterated greedy metaheuristic for the blocking job shop scheduling problem , 2016, J. Heuristics.

[54]  Yew-Soon Ong,et al.  Multifactorial Evolution: Toward Evolutionary Multitasking , 2016, IEEE Transactions on Evolutionary Computation.

[55]  S. Goodman,et al.  Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations , 2016, European Journal of Epidemiology.

[56]  Anne Auger,et al.  COCO: Performance Assessment , 2016, ArXiv.

[57]  Robert Ivor John,et al.  Good Laboratory Practice for optimization research , 2016, J. Oper. Res. Soc..

[58]  Jessika Daecher,et al.  Advanced Methods And Applications In Computational Intelligence , 2016 .

[59]  Leslie Pérez Cáceres,et al.  The irace package: Iterated racing for automatic algorithm configuration , 2016 .

[60]  Angel A. Juan,et al.  A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems , 2015 .

[61]  Witold Pedrycz,et al.  A variable reduction strategy for evolutionary algorithms handling equality constraints , 2015, Appl. Soft Comput..

[62]  Nickolas Savarimuthu,et al.  Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of Ant Colony Optimization and its variants , 2015, Artificial Intelligence Review.

[63]  Xiaodong Li,et al.  Designing benchmark problems for large-scale continuous optimization , 2015, Inf. Sci..

[64]  Antonio J. Nebro,et al.  Redesigning the jMetal Multi-Objective Optimization Framework , 2015, GECCO.

[65]  A. E. Eiben,et al.  From evolutionary computation to the evolution of things , 2015, Nature.

[66]  Thibaut Vidal,et al.  Hybrid metaheuristics for the Clustered Vehicle Routing Problem , 2014, Comput. Oper. Res..

[67]  Kenneth Sörensen,et al.  Metaheuristics - the metaphor exposed , 2015, Int. Trans. Oper. Res..

[68]  Cécile Murat,et al.  Recent advances in robust optimization: An overview , 2014, Eur. J. Oper. Res..

[69]  Marjan Mernik,et al.  Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them , 2014, Appl. Soft Comput..

[70]  José Francisco Aldana Montes,et al.  jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework , 2014, Bioinform..

[71]  Lars Kotthoff,et al.  Algorithm Selection for Combinatorial Search Problems: A Survey , 2012, AI Mag..

[72]  Sándor Danka,et al.  A statistically correct methodology to compare metaheuristics in resource-constrained Project Scheduling , 2013 .

[73]  Patrick Siarry,et al.  A survey on optimization metaheuristics , 2013, Inf. Sci..

[74]  Zhihua Cui,et al.  Swarm Intelligence and Bio-Inspired Computation: Theory and Applications , 2013 .

[75]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[76]  Enrique Alba,et al.  Parallel metaheuristics: recent advances and new trends , 2012, Int. Trans. Oper. Res..

[77]  Dong Zhou,et al.  Translation techniques in cross-language information retrieval , 2012, CSUR.

[78]  Giovanni Iacca,et al.  Three variants of three Stage Optimal Memetic Exploration for handling non-separable fitness landscapes , 2012, 2012 12th UK Workshop on Computational Intelligence (UKCI).

[79]  James Robertson,et al.  Mastering the Requirements Process: Getting Requirements Right , 2012 .

[80]  Jerry Swan,et al.  The automatic generation of mutation operators for genetic algorithms , 2012, GECCO '12.

[81]  A. Smith,et al.  Research Methodology: A Step-by-step Guide for Beginners , 2012 .

[82]  Giovanni Iacca,et al.  Ockham's Razor in memetic computing: Three stage optimal memetic exploration , 2012, Inf. Sci..

[83]  Michael Affenzeller,et al.  A Comprehensive Survey on Fitness Landscape Analysis , 2012, Recent Advances in Intelligent Engineering Systems.

[84]  M. Noel,et al.  A new gradient based particle swarm optimization algorithm for accurate computation of global minimum , 2012, Appl. Soft Comput..

[85]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[86]  R. Peng Reproducible Research in Computational Science , 2011, Science.

[87]  Antonio LaTorre,et al.  A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test , 2011, Soft Comput..

[88]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[89]  Jerry Swan,et al.  Automatically designing selection heuristics , 2011, GECCO.

[90]  Enrique Alba,et al.  Parallel Genetic Algorithms , 2011, Studies in Computational Intelligence.

[91]  Ponnuthurai N. Suganthan,et al.  Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art , 2011, Swarm Evol. Comput..

[92]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

[93]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[94]  Jano I. van Hemert,et al.  Discovering the suitability of optimisation algorithms by learning from evolved instances , 2011, Annals of Mathematics and Artificial Intelligence.

[95]  R.SIVARAJ,et al.  A REVIEW OF SELECTION METHODS IN GENETIC ALGORITHM , 2011 .

[96]  P. N. Suganthan,et al.  Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems , 2011 .

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

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

[99]  Kalyanmoy Deb,et al.  Reliability-Based Optimization Using Evolutionary Algorithms , 2009, IEEE Transactions on Evolutionary Computation.

[100]  F. Hutter,et al.  ParamILS: An Automatic Algorithm Configuration Framework , 2014, J. Artif. Intell. Res..

[101]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[102]  Stefan M. Wild,et al.  Benchmarking Derivative-Free Optimization Algorithms , 2009, SIAM J. Optim..

[103]  Kate Smith-Miles,et al.  Towards insightful algorithm selection for optimisation using meta-learning concepts , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[104]  Jeff Edmonds,et al.  Definition of Optimization Problems , 2008 .

[105]  Kenneth Sörensen,et al.  Adaptive and Multilevel Metaheuristics , 2008, Adaptive and Multilevel Metaheuristics.

[106]  Martin Glinz,et al.  On Non-Functional Requirements , 2007, 15th IEEE International Requirements Engineering Conference (RE 2007).

[107]  Colin R. Reeves,et al.  Evolutionary computation: a unified approach , 2007, Genetic Programming and Evolvable Machines.

[108]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[109]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[110]  Jing J. Liang,et al.  Problem Definitions for Performance Assessment of Multi-objective Optimization Algorithms , 2007 .

[111]  Sancho Salcedo-Sanz,et al.  Improving metaheuristics convergence properties in inductive query by example using two strategies for reducing the search space , 2007, Comput. Oper. Res..

[112]  Mike Preuss,et al.  Experiments on metaheuristics: Methodological overview and open issues , 2007 .

[113]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[114]  Jürgen Branke,et al.  Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation , 2006, IEEE Transactions on Evolutionary Computation.

[115]  Piero P. Bonissone,et al.  Evolutionary algorithms + domain knowledge = real-world evolutionary computation , 2006, IEEE Transactions on Evolutionary Computation.

[116]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[117]  Sigurdur Olafsson,et al.  Chapter 21 Metaheuristics , 2006, Simulation.

[118]  R. Steele,et al.  Optimization , 2005, Encyclopedia of Biometrics.

[119]  Enrique Alba,et al.  Parallel Metaheuristics: A New Class of Algorithms , 2005 .

[120]  Jürgen Branke,et al.  Evolutionary optimization in uncertain environments-a survey , 2005, IEEE Transactions on Evolutionary Computation.

[121]  Michel Gendreau,et al.  Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms , 2005, Transp. Sci..

[122]  Yaochu Jin,et al.  A comprehensive survey of fitness approximation in evolutionary computation , 2005, Soft Comput..

[123]  Mark T True,et al.  Software Requirements , 2005 .

[124]  Fernando Pérez-Cruz,et al.  Enhancing genetic feature selection through restricted search and Walsh analysis , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[125]  El-Ghazali Talbi,et al.  ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics , 2004, J. Heuristics.

[126]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[127]  Andrzej Jaszkiewicz,et al.  Evaluation of Multiple Objective Metaheuristics , 2004, Metaheuristics for Multiobjective Optimisation.

[128]  Uday Kumar Chakraborty,et al.  An analysis of Gray versus binary encoding in genetic search , 2003, Inf. Sci..

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

[130]  Bernhard Sendhoff,et al.  Trade-Off between Performance and Robustness: An Evolutionary Multiobjective Approach , 2003, EMO.

[131]  Vladimir Vacic,et al.  VEHICLE ROUTING PROBLEM WITH TIME WINDOWS , 2014 .

[132]  Bernhard Sendhoff,et al.  A framework for evolutionary optimization with approximate fitness functions , 2002, IEEE Trans. Evol. Comput..

[133]  Fernando Pérez-Cruz,et al.  Feature Selection via Genetic Optimization , 2002, ICANN.

[134]  Franz Rothlauf,et al.  Representations for genetic and evolutionary algorithms , 2002, Studies in Fuzziness and Soft Computing.

[135]  A. E. Eiben,et al.  A critical note on experimental research methodology in EC , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[136]  Michael Sampels,et al.  Ant colony optimization for FOP shop scheduling: a case study on different pheromone representations , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[137]  Christian M. Reidys,et al.  Combinatorial Landscapes , 2002, SIAM Rev..

[138]  Haym Hirsh,et al.  Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models , 2000, GECCO.

[139]  D. Hunter,et al.  Optimization Transfer Using Surrogate Objective Functions , 2000 .

[140]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[141]  Arkadi Nemirovski,et al.  Robust solutions of uncertain linear programs , 1999, Oper. Res. Lett..

[142]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[143]  Bernd Freisleben,et al.  Fitness landscapes and memetic algorithm design , 1999 .

[144]  Dorit S. Hochba,et al.  Approximation Algorithms for NP-Hard Problems , 1997, SIGA.

[145]  S. Ronald,et al.  Robust encodings in genetic algorithms: a survey of encoding issues , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[146]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[147]  Christian Bierwirth,et al.  On Permutation Representations for Scheduling Problems , 1996, PPSN.

[148]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[149]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[150]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[151]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[152]  Mihalis Yannakakis,et al.  Optimization, approximation, and complexity classes , 1991, STOC '88.

[153]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

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

[155]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

[156]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[157]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[158]  K. Pearson,et al.  Statistical Tests , 1935, Nature.