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

Hussain, 2019, Metaheuristic research: a comprehensive survey, Artif Intell Rev, 52, 2191, 10.1007/s10462-017-9605-z BoussaïD, 2013, A survey on optimization metaheuristics, Inf Sci (Ny), 237, 82, 10.1016/j.ins.2013.02.041 Kennedy, 2006, Swarm Intelligence, 187 Eiben, 2015, From evolutionary computation to the evolution of things, Nature, 521, 476, 10.1038/nature14544 Del Ser, 2019, Bio-inspired computation: where we stand and what’s next, Swarm Evol Comput, 48, 220, 10.1016/j.swevo.2019.04.008 Yang, 2018, Mathematical Analysis of Nature-inspired Algorithms, 1 Pranzo, 2016, An iterated greedy metaheuristic for the blocking job shop scheduling problem, Journal of Heuristics, 22, 587, 10.1007/s10732-014-9279-5 Vidal, 2015, Hybrid metaheuristics for the clustered vehicle routing problem, Computers & Operations Research, 58, 87, 10.1016/j.cor.2014.10.019 Danka, 2013, A statistically correct methodology to compare metaheuristics in resource-constrained project scheduling, Pollack Periodica, 8, 119, 10.1556/Pollack.8.2013.3.12 Kendall, 2016, Good laboratory practice for optimization research, Journal of the Operational Research Society, 67, 676, 10.1057/jors.2015.77 Jaszkiewicz, 2004, Evaluation of Multiple Objective Metaheuristics, 65 Chiarandini, 2007, Experiments on metaheuristics: Methodological overview and open issues Hochba, 1997, Approximation algorithms for np-hard problems, ACM Sigact News, 28, 40, 10.1145/261342.571216 Papadimitriou, 1998 Papadimitriou, 1991, Optimization, approximation, and complexity classes, J Comput Syst Sci, 43, 425, 10.1016/0022-0000(91)90023-X Blum, 2003, Metaheuristics in combinatorial optimization: overview and conceptual comparison, ACM computing surveys (CSUR), 35, 268, 10.1145/937503.937505 Dokeroglu, 2019, A survey on new generation metaheuristic algorithms, Computers & Industrial Engineering, 106040, 10.1016/j.cie.2019.106040 Yang, 2013 Bonabeau, 1999 De Jong, 2006 Goldberg, 1989 De Jong, 1975 Kennedy, 1995, Particle swarm optimization, volume 4, 1942 Dorigo, 1997, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput., 1, 53, 10.1109/4235.585892 Molina, 2020, Comprehensive taxonomies of nature-and bio-inspired optimization: inspiration versus algorithmic behavior, critical analysis and recommendations, arXiv preprint arXiv:2002.08136 Sörensen, 2015, Metaheuristics the metaphor exposed, International Transactions in Operational Research, 22, 3, 10.1111/itor.12001 Sörensen, 2018, A history of metaheuristics, Handbook of heuristics, 1 Derrac, 2011, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol Comput, 1, 3, 10.1016/j.swevo.2011.02.002 Bräysy, 2005, Vehicle routing problem with time windows, part i: route construction and local search algorithms, Transportation science, 39, 104, 10.1287/trsc.1030.0056 Osaba, 2018, Good practice proposal for the implementation, presentation, and comparison of metaheuristics for solving routing problems, Neurocomputing, 271, 2, 10.1016/j.neucom.2016.11.098 Eggensperger, 2019, Pitfalls and best practices in algorithm configuration, Journal of Artificial Intelligence Research, 64, 861, 10.1613/jair.1.11420 Eiben, 2002, A critical note on experimental research methodology in ec, volume 1, 582 Črepinšek, 2014, Replication and comparison of computational experiments in applied evolutionary computing: common pitfalls and guidelines to avoid them, Appl Soft Comput, 19, 161, 10.1016/j.asoc.2014.02.009 LaTorre, 2020, Fairness in bio-inspired optimization research: aprescription of methodological guidelines for comparing meta-heuristics, arXiv preprint arXiv:2004.09969 Hansen, 2016, Coco: performance assessment, arXiv preprint arXiv:1605.03560 Edmonds, 2008, 171 Huang, 2007, Problem definitions for performance assessment of multi-objective optimization algorithms Kumar, 2010 Jie, 2019, The two-echelon capacitated electric vehicle routing problem with battery swapping stations: formulation and efficient methodology, Eur J Oper Res, 272, 879, 10.1016/j.ejor.2018.07.002 Delorme, 2016, Bin packing and cutting stock problems: mathematical models and exact algorithms, Eur J Oper Res, 255, 1, 10.1016/j.ejor.2016.04.030 Glinz, 2007, On non-functional requirements, 21 Robertson, 2012 Sommerville, 2001, Software engineering, Ed., Harlow, UK.: Addison-Wesley Davis, 1993, Software requirements, OBJECTS FUNCTIONS & STATUS Coffman, 1996, 46 Lange, 2000, Optimization transfer using surrogate objective functions, Journal of computational and graphical statistics, 9, 1 Spagnol, 2019, Global sensitivity analysis for optimization with variable selection, SIAM/ASA Journal on Uncertainty Quantification, 7, 417, 10.1137/18M1167978 Boyd, 2004 Ponton, 2018, On time optimization of centroidal momentum dynamics, 5776 Wright, 1932, volume 1 Reidys, 2002, Combinatorial landscapes, SIAM Rev., 44, 3, 10.1137/S0036144501395952 Pitzer, 2012, A Comprehensive Survey on Fitness Landscape Analysis, 161 Merz, 1999, Fitness landscapes and memetic algorithm design, New ideas in optimization, 245 Ronald, 1997, Robust encodings in genetic algorithms: A survey of encoding issues, 43 Talbi, 2009, volume 74 Chakraborty, 2003, An analysis of gray versus binary encoding in genetic search, Inf Sci (Ny), 156, 253, 10.1016/S0020-0255(03)00178-6 Bierwirth, 1996, On permutation representations for scheduling problems, 310 Bean, 1994, Genetic algorithms and random keys for sequencing and optimization, ORSA journal on computing, 6, 154, 10.1287/ijoc.6.2.154 Rothlauf, 2006, Representations for Genetic and Evolutionary Algorithms, 9 Larranaga, 1999, Genetic algorithms for the travelling salesman problem: a review of representations and operators, Artif Intell Rev, 13, 129, 10.1023/A:1006529012972 Dorigo, 1999, Ant colony optimization: a new meta-heuristic, volume 2, 1470 Blum, 2002, Ant colony optimization for fop shop scheduling: a case study on different pheromone representations, volume 2, 1558 Osaba, 2018, Multi-objective optimization of bike routes for last-mile package delivery with drop-offs, 865 Salcedo-Sanz, 2002, Feature selection via genetic optimization, 547 Salcedo-Sanz, 2004, Enhancing genetic feature selection through restricted search and walsh analysis, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34, 398, 10.1109/TSMCC.2004.833301 Salcedo-Sanz, 2007, Improving metaheuristics convergence properties in inductive query by example using two strategies for reducing the search space, Computers & operations research, 34, 91, 10.1016/j.cor.2005.05.001 Gupta, 2015, Multifactorial evolution: toward evolutionary multitasking, IEEE Trans. Evol. Comput., 20, 343, 10.1109/TEVC.2015.2458037 Gupta, 2017, Insights on transfer optimization: because experience is the best teacher, IEEE Transactions on Emerging Topics in Computational Intelligence, 2, 51, 10.1109/TETCI.2017.2769104 Kirkpatrick, 1983, Optimization by simulated annealing, Science, 220, 671, 10.1126/science.220.4598.671 Glover, 1998, Tabu Search, 2093 Alba, 2005, volume 47 Alba, 2013, Parallel metaheuristics: recent advances and new trends, International Transactions in Operational Research, 20, 1, 10.1111/j.1475-3995.2012.00862.x Atashpaz-Gargari, 2007, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, 4661 Luque, 2011, volume 367 Cantú-Paz, 1998, A survey of parallel genetic algorithms, Calculateurs paralleles, reseaux et systems repartis, 10, 141 Karaboga, 2007, Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems, 789 Yang, 2009, Cuckoo search via lévy flights, 210 Das, 2011, Real-parameter evolutionary multimodal optimization a survey of the state-of-the-art, Swarm Evol Comput, 1, 71, 10.1016/j.swevo.2011.05.005 Yang, 2009, Firefly algorithms for multimodal optimization, 169 Sivaraj, 2011, A review of selection methods in genetic algorithm, International journal of engineering science and technology, 3, 3792 Prakasam, 2016, Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of ant colony optimization and its variants, Artif Intell Rev, 45, 97, 10.1007/s10462-015-9441-y Ólafsson, 2006, Metaheuristics, Handbooks in operations research and management science, 13, 633, 10.1016/S0927-0507(06)13021-2 Kerschke, 2019, Automated algorithm selection: survey and perspectives, Evol Comput, 27, 3, 10.1162/evco_a_00242 Wolpert, 1995, No free lunch theorems for search Iacca, 2012, Ockham’S razor in memetic computing: three stage optimal memetic exploration, Inf Sci (Ny), 188, 17, 10.1016/j.ins.2011.11.025 Caraffini, 2012, Three variants of three stage optimal memetic exploration for handling non-separable fitness landscapes, 1 Cotta, 2008, volume 136 Woodward, 2011, Automatically designing selection heuristics, 583 Woodward, 2012, The automatic generation of mutation operators for genetic algorithms, 67 Liu, 2020, Paradoxes in numerical comparison of optimization algorithms, IEEE Trans. Evol. Comput., 24, 777, 10.1109/TEVC.2019.2955110 Tanabe, 2020, An easy-to-use real-world multi-objective optimization problem suite, Appl Soft Comput, 89, 106078, 10.1016/j.asoc.2020.106078 Cheng, 2017, A benchmark test suite for evolutionary many-objective optimization, Complex & Intelligent Systems, 3, 67, 10.1007/s40747-017-0039-7 Chen, 2020, Proposal of a realistic many-objective test suite, 201 Picard, 2020, Realistic constrained multi-objective optimization benchmark problems from design, IEEE Trans. Evol. Comput. Kumar, 2020, A test-suite of non-convex constrained optimization problems from the real-world and some baseline results, Swarm Evol Comput, 100693, 10.1016/j.swevo.2020.100693 He, 2019, A repository of real-world datasets for data-driven evolutionary multiobjective optimization, Complex & Intelligent Systems, 1 Lou, 2019, On constructing alternative benchmark suite for evolutionary algorithms, Swarm Evol Comput, 44, 287, 10.1016/j.swevo.2018.04.005 Ishibuchi, 2019, A scalable multimodal multiobjective test problem, 310 Omidvar, 2015, Designing benchmark problems for large-scale continuous optimization, Inf Sci (Ny), 316, 419, 10.1016/j.ins.2014.12.062 Moré, 2009, Benchmarking derivative-free optimization algorithms, SIAM J. Optim., 20, 172, 10.1137/080724083 Liu, 2017, Benchmarking stochastic algorithms for global optimization problems by visualizing confidence intervals, IEEE Trans Cybern, 47, 2924, 10.1109/TCYB.2017.2659659 LaTorre, 2010, A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 15, 2187 Herrera-Poyatos, 2017, Genetic and memetic algorithm with diversity equilibrium based on greedy diversification, CoRR, abs/1702.03594 Črepinšek, 2013, Exploration and exploitation in evolutionary algorithms: a survey, ACM Comput Surv, 45, 1, 10.1145/2480741.2480752 McCabe, 1976, A complexity measure, IEEE Trans. Software Eng., SE-2, 308, 10.1109/TSE.1976.233837 Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, The Journal of Machine Learning Research, 7, 1 Greenland, 2016, Statistical tests, p values, confidence intervals, and power: a guide to misinterpretations, Eur. J. Epidemiol., 31, 337, 10.1007/s10654-016-0149-3 Benavoli, 2017, Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis, The Journal of Machine Learning Research, 18, 2653 Biedrzycki, 2019, On equivalence of algorithm’s implementations: The CMA-ES algorithm and its five implementations, 247 Killeen, 2019, Predict, control, and replicate to understand: how statistics can foster the fundamental goals of science, Perspectives on Behavior Science, 42, 109, 10.1007/s40614-018-0171-8 Peng, 2011, Reproducible research in computational science, Science, 334, 1226, 10.1126/science.1213847 Collaboration, 2013, The Reproducibility Project: A Model of Large-Scale Collaboration for Empirical Research on Reproducibility Scott, 2019, ECJ at 20: Toward a general metaheuristics toolkit, 1391 Wagner, 2014, Advanced Methods and Applications in Computational Intelligence, vol. 6, 197 Durillo, 2011, Jmetal: a java framework for multi-objective optimization, Adv. Eng. Software, 42, 760, 10.1016/j.advengsoft.2011.05.014 Nebro, 2015, Redesigning the jMetal multi-objective optimization framework, 1093 López-Camacho, 2013, Jmetalcpp: optimizing molecular docking problems with a c++ metaheuristic framework, Bioinformatics, 30, 437, 10.1093/bioinformatics/btt679 Benítez-Hidalgo, 2019, Jmetalpy: a python framework for multi-objective optimization with metaheuristics, Swarm Evol Comput, 51, 100598, 10.1016/j.swevo.2019.100598 D. Hadka, MOEA Framework. A Free and Open Source Java Framework for Multiobjective Optimization, 2020. http://moeaframework.org/. Vrbančič, 2018, Niapy: python microframework for building nature-inspired algorithms, Journal of Open Source Software, 3, 10.21105/joss.00613 F. Biscani, D. Izzo, pagmo, 2020. https://esa.github.io/pagmo2/. Cahon, 2004, Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics, Journal of Heuristics, 10.1023/B:HEUR.0000026900.92269.ec Tian, 2017, Platemo: a MATLAB platform for evolutionary multi-objective optimization, IEEE Comput Intell Mag, 12, 73, 10.1109/MCI.2017.2742868 F. Biscani, D. Izzo, pygmo, 2020. https://esa.github.io/pygmo2/. D. Hadka, Platypus - Multiobjective Optimization in Python, 2020. https://platypus.readthedocs.io/. Huang, 2020, A survey of automatic parameter tuning methods for metaheuristics, IEEE Trans. Evol. Comput., 24, 201, 10.1109/TEVC.2019.2921598 nez, 2016, The irace package: iterated racing for automatic algorithm configuration, Oper. Res. Perspect., 3, 43 Hutter, 2009, Paramils: an automatic algorithm configuration framework, J. Artif. Int. Res., 36, 267 Gabrel, 2014, Recent advances in robust optimization: an overview, Eur J Oper Res, 235, 471, 10.1016/j.ejor.2013.09.036 Jin, 2005, Evolutionary optimization in uncertain environments-a survey, IEEE Trans. Evol. Comput., 9, 303, 10.1109/TEVC.2005.846356 Paenke, 2006, Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation, IEEE Trans. Evol. Comput., 10, 405, 10.1109/TEVC.2005.859465 Ben-Tal, 1999, Robust solutions of uncertain linear programs, Operations research letters, 25, 1, 10.1016/S0167-6377(99)00016-4 Jin, 2003, Trade-off between performance and robustness: An evolutionary multiobjective approach, 237 Deb, 2009, Reliability-based optimization using evolutionary algorithms, IEEE Trans. Evol. Comput., 13, 1054, 10.1109/TEVC.2009.2014361 van der Blom, 2020, Towards realistic optimization benchmarks: aquestionnaire on the properties of real-world problems, arXiv preprint arXiv:2004.06395 Dunning, 2017, Jump: a modeling language for mathematical optimization, SIAM Rev., 59, 295, 10.1137/15M1020575 Noel, 2012, A new gradient based particle swarm optimization algorithm for accurate computation of global minimum, Appl Soft Comput, 12, 353, 10.1016/j.asoc.2011.08.037 Bonissone, 2006, Evolutionary algorithms+ domain knowledge= real-world evolutionary computation, IEEE Trans. Evol. Comput., 10, 256, 10.1109/TEVC.2005.857695 Fischetti, 2018, Matheuristics, 121 Wu, 2015, A variable reduction strategy for evolutionary algorithms handling equality constraints, Appl Soft Comput, 37, 774, 10.1016/j.asoc.2015.09.007 Das, 2010, Problem definitions and evaluation criteria for cec 2011 competition on testing evolutionary algorithms on real world optimization problems, Jadavpur University, Nanyang Technological University, Kolkata, 341 Juan, 2015, A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems, Oper. Res. Perspect., 2, 62 Chica, 2017, Why simheuristics? benefits, limitations, and best practices when combining metaheuristics with simulation, Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation (January 1, 2017) Jin, 2011, Surrogate-assisted evolutionary computation: recent advances and future challenges, Swarm Evol Comput, 1, 61, 10.1016/j.swevo.2011.05.001 Jin, 2005, A comprehensive survey of fitness approximation in evolutionary computation, Soft comput, 9, 3, 10.1007/s00500-003-0328-5 Rasheed, 2000, Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models, 628 Jin, 2002, A framework for evolutionary optimization with approximate fitness functions, IEEE Trans. Evol. Comput., 6, 481, 10.1109/TEVC.2002.800884 Bhosekar, 2018, Advances in surrogate based modeling, feasibility analysis, and optimization: a review, Computers & Chemical Engineering, 108, 250, 10.1016/j.compchemeng.2017.09.017 Arrieta, 2020, Explainable artificial intelligence (xai): concepts, taxonomies, opportunities and challenges toward responsible ai, Information Fusion, 58, 82, 10.1016/j.inffus.2019.12.012 Guo, 2018, A survey of learning causality with data: problems and methods, arXiv preprint arXiv:1809.09337 Moraffah, 2020, Causal interpretability for machine learning-problems, methods and evaluation, ACM SIGKDD Explorations Newsletter, 22, 18, 10.1145/3400051.3400058 Huang, 2020, A survey of automatic parameter tuning methods for metaheuristics, IEEE Trans. Evol. Comput., 24, 201, 10.1109/TEVC.2019.2921598 Smith-Miles, 2008, Towards insightful algorithm selection for optimisation using meta-learning concepts, 4118 Kotthoff, 2016, Algorithm Selection for Combinatorial Search Problems: A Survey, 149 Smith-Miles, 2011, Discovering the suitability of optimisation algorithms by learning from evolved instances, Ann Math Artif Intell, 61, 87, 10.1007/s10472-011-9230-5 Kanda, 2016, Meta-learning to select the best meta-heuristic for the traveling salesman problem: a comparison of meta-features, Neurocomputing, 205, 393, 10.1016/j.neucom.2016.04.027 Gutierrez-Rodríguez, 2019, Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning, Expert Syst Appl, 118, 470, 10.1016/j.eswa.2018.10.036 Pavelski, 2019, Meta-learning on flowshop using fitness landscape analysis, 925 Wu, 2019, Ensemble strategies for population-based optimization algorithms–a survey, Swarm Evol Comput, 44, 695, 10.1016/j.swevo.2018.08.015