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.
The field of metaheuristics has undergone several paradigm shifts that have changed the way researchers look upon the development of heuristic methods. Most notably, there has been a shift from the method-centric period, in which metaheuristics were thought of as algorithms, to the framework-centric period, in which researchers think of metaheuristics as more general high-level frameworks, i.e., consistent collections of concepts and ideas that offer guidelines on how to go about solving an optimization problem heuristically.
Tremendous progress has been made in the development of heuristics over the years. Optimization problems that seemed intractable only a few decades ago can now be efficiently solved. Nevertheless, there is still much room for evolution in the research field, an evolution that will allow it to move into the scientific period. In this period, we will see more structured knowledge generation that will benefit both researchers and practitioners.
Unfortunately, a significant fraction of the research community has deluded itself into thinking that scientific progress can be made by resorting to ever more outlandish metaphors as the basis for so-called “novel” methods. Even though considerable damage to the research field will have been inflicted by the time these ideas have been stamped out, there is no doubt that science will ultimately prevail.
This is a preview of subscription content, log in to check access.
Chu T (2014) Human purpose and transhuman potential: a cosmic vision for our future evolution. Origin Press, San Rafael. ISBN:978-1-57983-0250Google Scholar
Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. In: Varela FJ, Bourgine P (eds) Proceedings of the first European conference on artificial life. MIT Press, Cambridge, pp 134–142Google Scholar
Corberán Á, Peiró J, Campos V, Glover F, Martí R (2016) Strategic oscillation for the capacitated hub location problem with modular links. J Heuristics 22 (2): 221–244CrossRefGoogle Scholar
Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. MIT Press, Cambridge. ISBN:978-0-262-03384-8zbMATHGoogle Scholar
Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New YorkzbMATHGoogle Scholar
García-Martínez C, Rodriguez FJ, Lozano M (2014) Tabu-enhanced iterated greedy algorithm: a case study in the quadratic multiple knapsack problem. Eur J Oper Res 232 (3): 454–463MathSciNetCrossRefzbMATHGoogle Scholar
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts – towards memetic algorithms. Technical Report 826, Caltech Concurrent Computation Program, PasadenaGoogle Scholar
Polya G (2014) How to solve it: a new aspect of mathematical method. Princeton university press, PrincetonzbMATHGoogle Scholar
Ribeiro CC, Rosseti I, Souza RC (2011) Effective probabilistic stopping rules for randomized metaheuristics: GRASP implementations. In: Learning and intelligent optimization. Lecture notes in computer science, vol 6683. Springer Science & Business Media, pp 146–160. https://doi.org/10.1007/978-3-642-25566-3_11
Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp Sci 40 (4): 455–472CrossRefGoogle Scholar
Ruiz R, Stützle T (2008) An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur J Oper Res 187 (3): 1143–1159CrossRefzbMATHGoogle Scholar
Yagiura M, Iwasaki S, Ibaraki T, Glover F (2004) A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem. Discret Optim 1 (1): 87–98CrossRefzbMATHGoogle Scholar