A History of Metaheuristics

This chapter describes the history of metaheuristics in five distinct periods, starting long before the first use of the term and ending a long time in the future.

[1]  George E. P. Box,et al.  Evolutionary Operation: a Method for Increasing Industrial Productivity , 1957 .

[2]  Allen Newell,et al.  Heuristic Problem Solving: The Next Advance in Operations Research , 1958 .

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

[4]  Harvey H. Shore.  THE TRANSPORTATION PROBLEM AND THE VOGEL APPROXIMATION METHOD , 1970 .

[5]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

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

[7]  F. Glover.  HEURISTICS FOR INTEGER PROGRAMMING USING SURROGATE CONSTRAINTS , 1977 .

[8]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

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

[10]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

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

[12]  Ingo Rechenberg,et al.  Evolution Strategy: Nature’s Way of Optimization , 1989 .

[13]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[14]  Harvey J. Greenberg,et al.  New approaches for heuristic search: A bilateral linkage with artificial intelligence , 1989 .

[15]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..

[16]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[17]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[18]  Marco Dorigo,et al.  Distributed Optimization by Ant Colonies , 1992 .

[19]  David L. Woodruff,et al.  Hashing vectors for tabu search , 1993, Ann. Oper. Res..

[20]  G. Dueck.  New optimization heuristics , 1993 .

[21]  Mirko Krivánek,et al.  Simulated Annealing: A Proof of Convergence , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[23]  James P. Kelly,et al.  A scatter-search-based learning algorithm for neural network training , 1996, J. Heuristics.

[24]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

[25]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[26]  Fred W. Glover,et al.  A Template for Scatter Search and Path Relinking , 1997, Artificial Evolution.

[27]  Colin R. Reeves,et al.  Genetic Algorithms: Principles and Perspectives: A Guide to Ga Theory , 2002 .

[28]  L. Darrell Whitley,et al.  Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance , 2002, INFORMS J. Comput..

[29]  L. Darrell Whitley,et al.  Problem difficulty for tabu search in job-shop scheduling , 2003, Artif. Intell..

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

[31]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[32]  P. Campbell.  How to Solve It: A New Aspect of Mathematical Method , 2005 .

[33]  Xin Yao,et al.  Time complexity of evolutionary algorithms for combinatorial optimization: A decade of results , 2007, Int. J. Autom. Comput..

[34]  Orhan Dengiz,et al.  A tabu search algorithm for the training of neural networks , 2009, J. Oper. Res. Soc..

[35]  Dennis Weyland,et al.  A Rigorous Analysis of the Harmony Search Algorithm: How the Research Community can be Misled by a "Novel" Methodology , 2010, Int. J. Appl. Metaheuristic Comput..

[36]  Celso C. Ribeiro,et al.  Effective Probabilistic Stopping Rules for Randomized Metaheuristics: GRASP Implementations , 2011, LION.

[37]  J. Carbonell,et al.  Learning by Analogy: Formulating and Generalizing Plans from Past Experience , 1983 .

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

[39]  Nenad Mladenovic,et al.  Variable neighborhood search for location routing , 2013, Comput. Oper. Res..

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

[41]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[42]  Fred W. Glover,et al.  The unconstrained binary quadratic programming problem: a survey , 2014, Journal of Combinatorial Optimization.

[43]  Li Zhao,et al.  A review of opposition-based learning from 2005 to 2012 , 2014, Eng. Appl. Artif. Intell..

[44]  Marc Sevaux,et al.  Solving dynamic memory allocation problems in embedded systems with parallel variable neighborhood search strategies , 2015, Electron. Notes Discret. Math..

[45]  Panos M. Pardalos,et al.  Iterated local search embedded adaptive neighborhood selection approach for the multi-depot vehicle routing problem with simultaneous deliveries and pickups , 2015, Expert Syst. Appl..

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

[47]  Panos M. Pardalos,et al.  An adaptive simplified human learning optimization algorithm , 2015, Inf. Sci..

[48]  Fred W. Glover,et al.  A tabu search algorithm for cohesive clustering problems , 2015, J. Heuristics.

[49]  André Rossi,et al.  Robust scheduling of wireless sensor networks for target tracking under uncertainty , 2016, Eur. J. Oper. Res..

[50]  Eldad Haber,et al.  Building an iterative heuristic solver for a quantum annealer , 2015, Comput. Optim. Appl..

[51]  Fred W. Glover,et al.  Multi-wave algorithms for metaheuristic optimization , 2016, J. Heuristics.

[52]  Roberto Aringhieri,et al.  Local search metaheuristics for the critical node problem , 2016, Networks.

[53]  Flávio Keidi Miyazawa,et al.  Heuristics for a hub location‐routing problem , 2016, Networks.

[54]  Eric Bourreau,et al.  Partial target coverage to extend the lifetime in wireless multi‐role sensor networks , 2016, Networks.

[55]  Xenophon Papademetris,et al.  A simple and efficient strategy for solving very large-scale generalized cable-trench problems , 2016, Networks.

[56]  Kathryn E. Stecke,et al.  Mitigating disruptions in a multi-echelon supply chain using adaptive ordering , 2017 .

[57]  Stefan Voß,et al.  An equi‐model matheuristic for the multi‐depot ring star problem , 2016, Networks.

[58]  Narayan Rangaraj,et al.  Mathematical models and empirical analysis of a simulated annealing approach for two variants of the static data segment allocation problem , 2016, Networks.

[59]  André Rossi,et al.  A Two-Level solution approach to solve the Clustered Capacitated Vehicle Routing Problem , 2016, Comput. Ind. Eng..

[60]  Panos Pardalos,et al.  Heuristics for the network design problem with connectivity requirements , 2016, J. Comb. Optim..

[61]  Edoardo Amaldi,et al.  Metaheuristics for a job scheduling problem with smoothing costs relevant for the car industry , 2016, Networks.

[62]  Fred W. Glover,et al.  A learning-based path relinking algorithm for the bandwidth coloring problem , 2016, Eng. Appl. Artif. Intell..

[63]  Kenneth Sörensen,et al.  An iterated local search algorithm for water distribution network design optimization , 2016, Networks.

[64]  Abdelhakim Artiba,et al.  Nested general variable neighborhood search for the periodic maintenance problem , 2015, Eur. J. Oper. Res..

[65]  Shanlin Yang,et al.  Solving a supply chain scheduling problem with non-identical job sizes and release times by applying a novel effective heuristic algorithm , 2016, Int. J. Syst. Sci..

[66]  Steven P. Reinhardt,et al.  Partitioning Optimization Problems for Hybrid Classical/Quantum Execution TECHNICAL REPORT , 2017 .

[67]  Bassem Jarboui,et al.  A general variable neighborhood search for the swap-body vehicle routing problem , 2017, Comput. Oper. Res..

[68]  Fred W. Glover,et al.  Effective metaheuristic algorithms for the minimum differential dispersion problem , 2017, Eur. J. Oper. Res..

[69]  Fred W. Glover,et al.  Pseudo-centroid clustering , 2016, Soft Comput..

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

[71]  Fred W. Glover,et al.  Diversification-based learning in computing and optimization , 2017, J. Heuristics.