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A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, 2013). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, variable neighborhood search, (adaptive) large neighborhood search, and ant colony optimization, although many more exist. A problem-specific implementation of a heuristic optimization algorithm according to the guidelines expressed in a metaheuristic framework is also referred to as a metaheuristic. The term was coined by Glover (1986) and combines the Greek prefix meta- (metá, beyond in the sense of high-level) with heuristic (from the Greek heuriskein or euriskein, to search).

Metaheuristic algorithms, i.e., optimization methods designed according to the strategies laid out in a metaheuristic framework, are — as the name suggests — always heuristic in nature. This fact distinguishes them from exact methods, that do come with a proof that the optimal solution will be found in a finite (although often prohibitively large) amount of time. Metaheuristics are therefore developed specifically to find a solution that is “good enough” in a computing time that is “small enough”. As a result, they are not subject to combinatorial explosion – the phenomenon where the computing time required to find the optimal solution of NP-hard problems increases as an exponential function of the problem size.

Metaheuristics have been demonstrated by the scientific community to be a viable, and often superior, alternative to more traditional (exact) methods of mixed-integer optimization such as branch and bound and dynamic programming. Especially for complicated problems or large problem instances, metaheuristics are often able to offer a better trade-off between solution quality and computing time. Moreover, metaheuristics are more flexible than exact methods in two important ways. First, because metaheuristic frameworks are defined in general terms, metaheuristic algorithms can be adapted to fit the needs of most real-life optimization problems in terms of expected solution quality and allowed computing time, which can vary greatly across different problems and different situations. Secondly, metaheuristics do not put any demands on the formulation of the optimization problem (like requiring constraints or objective functions to be expressed as linear functions of the decision variables). However, this flexibility comes at the cost of requiring considerable problem-specific adaptation to achieve good performance.

The research field of metaheuristics is not without its critics – most of whom attack the perceived lack of universally applicable design methodology, the lack of scientific rigor in testing and comparing different implementations, and the tendency to create overly intricate methods with many different operators. Some authors have also criticized the way in which metaphors are used by some authors to motivate the development of "novel" methods (see Section 2.5 "the metaphor controversy").

Notwithstanding this criticism, it is hard to argue with success. The ability to obtain good solutions where other methods fail has made metaheuristics the method of choice for solving a majority of large real-life optimization problems, both in academic research and in practical applications. As a result, several commercial software vendors have implemented metaheuristics as their primary optimization engines, both in specialized software packages for production scheduling, vehicle routing (Sörensen et al., 2008) and nurse rostering (Burke et al., 2004) as well as in general-purpose optimization and simulation packages (April et al., 2003, Fu, 2002, Glover et al., 1999).

The underlying foundations of different metaheuristics vary significantly. Some model the optimization process by using a metaphor seemingly unrelated to optimization, such as natural evolution (genetic/evolutionary algorithms), the cooling of a crystalline solid (simulated annealing), or the behavior of animal swarms (e.g., ant colony optimization). Others, like tabu search, do not use such an intermediary level of explanation, but rather focus on exploiting the problem structure to improve the search for good solutions. In general, metaheuristics frameworks rely heavily on the use of randomness, although some completely deterministic strategies have also been proposed. Most metaheuristic frameworks have their origin in the 80’s (though in some cases roots can be traced to the mid 60s and 70s) and have enjoyed a steady rise in both use and popularity since the early 80’s. The metaheuristics field is currently the subject of a number of dedicated journals and conferences. EU/ME – the metaheuristics community [1] is the EURO-sponsored working group on metaheuristics and, with about 1400 members, the largest platform for communication among metaheuristics researchers worldwide.

This article provides a very high-level survey of a field that now counts many thousands of researchers, several dedicated journals and conference series, and an impressive number of publications each year. It is therefore incomplete by design. The article is also not a tutorial on metaheuristics, and may provide little insights for absolute newcomers to the field. The design of a heuristic algorithm for combinatorial optimization is something that is generally learned through experience. The authors would advise to start with a simple local search or constructive heuristic and add complexity (a perturbation move, a tabu list, another local search operator, ...) when this does not suffice.

A taxonomy of metaheuristics

Metaheuristic algorithms attempt to find the best (feasible) solution out of all possible solutions of an optimization problem. To this end, they evaluate potential solutions and perform a series of operations on them in order to find different, better solutions. Metaheuristics operate on a representation or encoding of a solution, an object that can be stored in computer memory and can be conveniently manipulated by the different operators employed by the metaheuristic. Three fundamental classes of metaheuristics can be distinguished, based on the way in which solutions are manipulated. Local search metaheuristics iteratively make small changes to a single solution. Constructive metaheuristics construct solutions from their constituting parts. Population-based metaheuristics iteratively combine solutions into new ones. However, these classes are not mutually exclusive and many metaheuristic algorithms combine ideas from different classes. Such methods are called hybrid metaheuristics.

Local search metaheuristics

Local search (LS), or iterative improvement, finds good solutions by iteratively making changes to a single solution, called the current (or incumbent) solution. These changes are called moves and are typically “small” (so that adjacent solutions are relatively close to each other according to a natural metric), hence the name of this class of metaheuristics. The set of solutions that can be obtained by applying a single move to a given solution is called the neighborhood of that solution. Depending on the way the solution is represented, different move types can be defined. Each move type gives rise to a neighborhood structure. In each iteration, the current solution is replaced by a solution from its neighborhood. The rule used to select the new current solution is called the move strategy or search strategy. A common search strategy is the steepest descent or steepest ascent strategy, in which the best move from the neighborhood is selected. Metaheuristics that use this strategy are often called hill-climbers. The mildest descent/ascent strategy selects the solution that improves the current solution by the smallest amount. Other move strategies include the first improving, strategy, in which the first move is selected that improves the current solution (which, of course, depends on the order in which the moves are checked).

One of the first metaheuristics developed, simulated annealing (SA) uses a move strategy that mimics the annealing process of a crystalline solid. At each iteration, a random solution \(x'\) is selected from the neighborhood of the current solution \(x\) This solution is “accepted” as the new current solution with probability \(e^{-[f(x')-f(x)]/T}\) where \(f(.)\) is the objective function value (to be maximized) of the solution between brackets and T is an endogenous parameter called the temperature. The probability of a solution being accepted is therefore higher if the solution is better, but also if the temperature is higher. The temperature is initially set to a high value, which leads to higher acceptance probabilities, and then gradually lowered as the search progresses (although it may be increased again at certain moments during the search). The function that describes the evolution of \(T\) throughout the different iterations is called the cooling schedule. Simulated annealing was first described in (Kirkpatrick et al., 1983), based upon an algorithm by Metropolis et al. (1953).

A solution that is better than any solution in its neighborhood is called a local optimum (as opposed to a global optimum, i.e., a best possible solution to the optimization problem). When the current solution is a local optimum, a metaheuristic will use a strategy to “escape” this local optimum. It is this strategy that characterizes a metaheuristic, and usually the name of the metaheuristic is derived from it. Local search metaheuristics are frameworks that rely mainly on iterative improvement to find good solutions.

Two simple, but commonly used, strategies are to apply a large random change (called perturbation) to the current solution or restart the search from a new random solution altogether. These strategies are called iterated local search (ILS) or multi-start local search (MLS) respectively (Lourenco et al., 2003).

A second strategy is motivated by the fact that a local optimum relative to a specific move type can often be improved by using another move type. To exploit this fact, some metaheuristics define different move types and change the move type used once a local optimum has been reached. Such metaheuristics are commonly called variable neighborhood search (VNS) algorithms (Mladenović and Hansen, 1997). However, using more than one neighborhood is far more common in the metaheuristics literature and not restricted to algorithms labeled VNS (Sörensen et al., 2008).

A third strategy to find good solutions is to use information on the past progress of the search and record this information in memory structures. Metaheuristics that use this strategy are commonly grouped under the umbrella term tabu search (TS) (Glover, 1989, 1990, 1996) algorithms (sometimes also called adaptive memory programming algorithms). Various types of memory structures are commonly used to remember specific properties of the trajectory through the search space that the algorithm has undertaken. A tabu list (from which the name of the metaheuristic framework derives) records the last encountered solutions (or some attributes of them) and forbids these solutions (or solutions containing one of the attributes) from being visited again as long as they are on the list. Alternatively, the tabu list may also record the last moves that have been made for the purpose of preventing them from being reversed. Whereas a tabu list can be viewed as a type of short-term memory, that records information on recently visited solutions, frequency memory is used as a type of long-term memory. This memory structure records how often certain attributes have been encountered in solutions on the search trajectory, which allows the search to avoid visiting solutions that display the most often encountered attributes or to visit solutions with attributes seldom encountered. The decision on how to use the frequency memory can be based on the quality of the solutions in which the attributes were found, e.g., favoring attributes found in high-quality solutions. The metaheuristic called guided local search (GLS) (Voudouris and Tsang, 1999) introduces a different type of memory, called an augmented objective function, that includes a penalty factor for each potential element. When a local optimum is reached, the penalty factor for for all elements of the current solution is increased, which makes other elements (and therefore other moves) more attractive. This in turn allows the search to escape from the local optimum. (Tabu search also sometimes employs penalties as a way of implementing tabu restrictions.)

Constructive metaheuristics

Constructive metaheuristics, as their name suggests, construct solutions from their constituting elements rather than improving complete solutions. This is done by adding one element at a time to a partial solution. Constructive metaheuristics are often adaptations of greedy algorithms that add the best possible element at each iteration. To improve the quality of the final solutions, most constructive metaheuristics include a local search phase after the construction phase.

GRASP, the acronym for greedy randomized adaptive search procedure (Feo and Resende, 1995), dampens the greediness of a constructive metaheuristic by using randomization. The most common variant of GRASP uses the following strategy. At each iteration, a restricted candidate list is updated, that contains the \(\alpha\) best elements that can be added to the partial solution. A random element is selected from this list for addition, after which the list is updated to reflect the new situation. The parameter \(\alpha\) determines the “greediness” of the search: if \(\alpha\) equals 1, the search is completely greedy whereas if \(\alpha\) is equal to the number of elements that can be added, the search is completely random. GRASP algorithms are often combined with a path relinking strategy (discussed later), see e.g., Commander et al. (2008), Nascimento et al. (2010), Resende et al. (2010).

Another way to improve the performance of the construction process, without resorting to randomness, is by using memory. Notable examples of metaheuristics that do this can be found in Fleurent and Glover (1999) and Glover et al. (2000). Similarly, look-ahead strategies (Pearl, 1984) evaluate the elements that can be added by considering the effect not only of the next move, but of several moves into the future. The pilot method (Duin and Voß, 1999), for example, is a look-ahead method that uses a constructive heuristic to determine the value of a potential element by generating a complete solution from the current partial solution with this element added.

Large neighborhood search (LNS) was introduced in (Shaw, 1998) and can be seen as the constructive counterpart of variable neighborhood search. It works by alternatingly destroying a solution and rebuilding it, usually using an extensive set of destroy and repair heuristics. In adaptive large neighborhood search (Pisinger and Ropke, 2007), the usage frequency of the various destroy and repair heuristics depends (usually in a stochastic way) on their previous performance: those destroy/repair heuristic pairs that have in the past led to good solutions are used more frequently than those that have not.

Ant colony optimization (ACO) (Dorigo et al., 1996, 2006) is an umbrella term for a set of related constructive metaheuristics that build solutions by mimicking the foraging behavior of ants. To this end, an external parameter for each potential element (called the pheromone level) is introduced. A pheromone is a chemical factor that triggers a social response to other animals of the same species. Ant colony optimization employs multiple artificial agents (called ants) that each construct a solution in parallel. Once each ant has constructed a solution, the pheromone level of each element in this solution is updated to allocate more pheromone to elements that lie in better solutions. This information is then used in the construction process of ACO, which selects elements based on a combination of the value of that element and its pheromone level. The process of ants constructing solutions is repeated, and elements that were present in high quality solutions will receive a larger probability of being selected as a result of their higher pheromone levels. Periodically, the pheromone level of all elements is reduced to reflect evaporation. Ant colony optimization has received and continues to receive widespread attention in the popular press (e.g., Anonymous, 2010), probably as a result of the intuitive appeal of the metaphor.

Population-based metaheuristics

Population-based metaheuristics find good solutions by iteratively selecting and then combining existing solutions from a set, usually called the population. The most important members of this class are evolutionary algorithms (EA) because they mimick the principles of natural evolution. We use the term evolutionary algorithms as an umbrella term to encompass the wide range of metaheuristics based on evolution. This includes genetic algorithms (GA) (Goldberg et al., 1989, Holland, 1975) genetic/evolutionary programming (GP/EP) (Koza, 1992), evolutionary computation (EC) (Fogel, 2006) evolution strategies (ES) (Beyer and Schwefel, 2002), and many others. When applied to combinatorial optimization problems, "pure" evolutionary algorithms are rare and most include some improvement operator, usually in the form of local search.

Evolutionary algorithms operate on a set or population of solutions and use two mechanisms to search for good solutions: the selection of predominantly high-quality solutions from the population and the recombination of those solutions into new ones, using specialized operators that combine the attributes of two or more solutions. After recombination, new solutions are reinserted into the population, possibly requiring them to satisfy conditions such as feasibility or minimum quality demands, to replace other (usually low-quality) solutions. Operators used in evolutionary algorithms (selection, recombination and reinsertion) almost without exception make heavy use of randomness. A mutation operator that randomly makes a (small) change to a solution after it has been recombined, is also frequently applied. Most evolutionary algorithms iterate the selection, recombination, mutation, and reinsertion phases a number of times, and report the best solution in the population. Evolutionary algorithms generally require some form of "population management" to ensure that the best solutions survive through the various iterations, while at the same time diversity is maintained in the population.

Deterministic population-based alternatives for evolutionary algorithms are scatter search and path relinking (Glover et al., 2000, 2003). Scatter search (SS) encodes solutions as (rounded) real-valued vectors and finds new solutions by generating convex or concave linear combinations of these vectors. Path relinking introduces the concept of a path between high-quality solutions, essentially a generalization of the concept of linear combination. Paths consist of elementary moves such as the ones used in local search metaheuristics. The moves on a path transform one solution (called the initiating solution) into a second solution (called the guiding solution) one move at a time. Path relinking can therefore be considered a local search heuristic that uses a move strategy in which the move to execute is chosen based on the fact that this move will bring the solution “closer” to the guiding solution. The selection of initiating and guiding solutions from a population (called the reference set), as well as the updating of the reference set once new solutions have been generated, are done according to deterministic rules in both path relinking and scatter search.

“Hybdrid” metaheuristics

In recent years, there is a tendency to view metaheuristic frameworks as providing general ideas or components that can be used to build optimization methods, rather than as cook book recipes that need to be closely followed (Michalewicz and Fogel, 2004). As a result, most recent metaheuristic algorithms combine ideas from different classes and the term hybrid metaheuristic has lost most of its discriminatory power. Many modern metaheuristics use specialized heuristics to efficiently solve subproblems produced by the metaheuristic method (e.g., Gendreau et al., 1994). Similarly, a large number of local search metaheuristics use a construction phase to find an initial solution (or a set of initial solutions) from which to start the neighborhood search. In fact the original description of the GRASP metaheuristic (Feo and Resende, 1995) prescribes a local search phase to follow the greedy randomized construction phase.

Algorithms belonging to the class of memetic algorithms (the only type of hybrid metaheuristic that has been given a specific name) (Moscato, 1989) combine recombination operators from the class of evolutionary algorithms with local search (meta)heuristics.

The metaphor controversy

A large (and increasing) number of publications focuses on the development of (supposedly) new metaheuristic frameworks based on metaphors. The list of natural or man-made processes that has been used as the basis for a metaheuristic framework now includes such diverse processes as bacterial foraging, river formation, biogeography, musicians playing together, electromagnetism, gravity, colonization by an empire, mine blasts, league championships, clouds, and so forth. An important subcategory is found in metaheuristics based on animal behavior. Ants, bees, bats, wolves, cats, fireflies, eagles, vultures, dolphins, frogs, salmon, vultures, termites, flies, and many others, have all been used to inspire a "novel" metaheuristic. A more complete list can be found in Fister et al (2013). As a general rule, publication of papers on metaphor-based metaheuristics has been limited to second-tier journals and conferences, but some recent exceptions to this rule can be found.

Sörensen (2013) states that research in this direction is fundamentally flawed. Most importantly, the author contends that the novelty of the underlying metaphor does not automatically render the resulting framework "novel". On the contrary, there is increasing evidence that very few of the metaphor-based methods are new in any interesting sense. Weyland (2013) demonstrates convincingly, that Harmony Search (Geem, 2001) is nothing more than a special case of Evolution Strategies (Beyer, 2002) in which each of the concepts of Evolution Strategies has been relabeled from an evolution-inspired term to a term inspired by musicians playing together. Even though the development of Evolution Strategies precedes that of Harmony Search by at least 30 years, the latter is proposed as an innovation and has by now attracted an impressively long list of follow-up research.

Recently several journals, including the flagship Journal of Heuristics, have revised their editorial policies to prevent methods whose only novelty is the metaphor they are based upon from being published.

Metaheuristics and exact methods

Algorithmic developments in both metaheuristics and exact methods have recently drawn the two fields closely together, and combinations of metaheuristic components (usually local search) with exact methods for (mixed integer) linear programming are now common. Sometimes called matheuristics, the resulting methods often integrate existing exact procedures to solve subproblems generated by a decomposition strategy, a restriction strategy or a relaxation strategy (see, e.g., Glover and Klingman, 1988, Rego, 2005). The results of solving these subproblems are used to guide a higher-level heuristic (Dumitrescu and Stützle, 2009, Raidl and Puchinger, 2008).

Several additional ways in which exact methods can improve the performance of metaheuristics have been reported. Exact methods can sometimes solve small instances of a complex problem effectively. A metaheuristic may operate by constructing collections of such small instances as a strategy for generating “structured moves” that transition from a given solution to a new one (see, e.g., Glover, 2005). Also, an exact method can be run for a very long time to obtain optimal solutions (at least to some instances of a problem class), and these optimal solutions can be used in the learning approach called target analysis (Glover, 1990, Glover and Laguna, 1997) as a way to produce improved decision rules for both metaheuristics and exact methods.

The result of combining a metaheuristic and an exact method does not necessarily have to be a heuristic method. Metaheuristics can be integrated with exact methods to improve the performance of the exact methods (Friden et al., 1989, Glover, 1990, Puchinger et al., 2009).

In a similar way, ideas and operators from constraint programming techniques have been integrated with metaheuristics, such as in the approach called constraint-based local search (Van Hentenryck and Michel, 2009).

Metaheuristics for different optimization problems

Continuous optimization

Metaheuristics are predominantly used for combinatorial optimization, but can be effectively adapted for continuous optimization, although this adaptation process is more involved for some metaheuristics than for others. Scatter search (Glover et al., 2000), particle swarm optimization (Kennedy et al., 1995) and an evolutionary approach called differential evolution (Storn and Price, 1997) are very naturally adapted to continuous problem domains. Most constructive and local search approaches on the other hand, require a considerable adaptation from their original formulation. Nonetheless, algorithms for continuous optimization based on tabu search (Chelouah and Siarry, 2000, Glover, 1994), GRASP (Hirsch et al., 2007), variable neighborhood search (Liberti and Drazič, 2005), and others, have been proposed. The field of metaheuristics for continuous optimization has developed more or less independently from the field of combinatorial optimization, and a more complete reflection on the achievements in this field is out of the scope of this article.

Multi-objective optimization

Many optimization problems have multiple (conflicting) objectives, essentially rendering the concept of optimality meaningless since the best solution for one objective may not be the best for another. In multi-objective optimization the concept of dominance is therefore introduced. A solution is said to dominate another solution if its quality is at least as good on every objective and better on at least one. The set of all non-dominated solutions of an optimization problem is called the Pareto set and the projection of this set onto the objective function space is called the Pareto front.

The aim of multi-objective metaheuristics is to approximate the Pareto front as closely as possible (Zitzler et al., 2004) and therefore generate a set of mutually non-dominated solutions called the Pareto set approximation. Examples of evolutionary multi-objective metaheuristics are the vector evaluated genetic algorithm (VEGA) (Schaffer, 1985), the non-dominated sorting algorithm (NDSA) (Srinivas and Deb, 1994), the multi-objective genetic algorithm (MOGA) (Fonseca and Fleming, 1993) and the improved strength pareto evolutionary algorithm (SPEA2) (Zitzler and Thiele, 1999). Currently, the non-dominated sorting genetic algorithm II (NSGA-II), introduced in (Deb, 2002) seems to be the most popular.

Notwithstanding some exceptions (e.g., Czyżak et al., 1998, Hansen, 1997), most multi-objective metaheuristics belong to the class of evolutionary algorithms (Jones et al., 2002). This can be explained by observing that these algorithms naturally operate on a set of solutions. Recently, however, competitive multi-objectives based on local search have been proposed (Tricoire, 2012).

Stochastic optimization

Stochastic (combinatorial) optimization problems include uncertain, stochastic or dynamic information in their parameters. The objective function value and the violation of constraints of such problems are therefore random variables. Evaluating a solution’s objective function value and/or it’s feasibility can be done either exactly (if a closed-form expression is available), by approximation or by Monte Carlo simulation. Metaheuristics using each of these possibilities have been proposed to solve different stochastic problems (Gutjahr, 1996, Bianchi et al., 2009, Ribeiro and Resende, 2010). A more elaborate survey of this field is beyond the scope of this article.

Research in metaheuristics


MIC, the metaheuristics international conference is the premier conference on metaheuristics. A yearly EU/MEeting is organized by EU/ME in collaboration with a research group and focuses on a specific (and changing) topic. The Matheuristics conference series has recently emerged to discuss combinations of metaheuristics with exact methods. The Learning and Intelligent Optimization conferences aim at exploring the boundaries between machine learning, artificial intelligent, mathematical programming and algorithms for optimization.

Many conferences are dedicated exclusively to evolutionary algorithms. These include Parallel Problem Solving From Nature (PPSN), the Genetic and Evolutionary Computation Conference (GECCO), EvoStar (a multi-conference comprising EuroGP, EvoCOP, EvoBIO, and EvoApplications), Evolutionary Multi-Criterion Optimization (EMO), and the IEEE Congress on Evolutionary Computation (CEC).

The Ants conference series focuses on research in swarm intelligence methods.

Besides these specialized conferences, metaheuristics hold a prominent position in general Operations Research conferences such as INFORMS, IFORS, and EURO.


Several scientific journals are dedicated to the topic of metaheuristics. The Journal of Heuristics is the most important one, and also the oldest journal to exclusively focus on (meta)heuristics. Two relatively young journals, the International Journal of Metaheuristics and the International Journal of Applied Metaheuristic Computing (IJAMC)), have recently been founded. However, a large majority of articles on metaheuristics are published in general Operations Research journals. Again, the field of evolutionary algorithms has its own share of journals: Evolutionary Computation, IEEE Transactions on Evolutionary Computation, Genetic Programming and Evolvable Machines, and the Journal of Artificial Evolution and Applications.

The swarm intelligence area has a dedicated journal appropriately called Swarm Intelligence.

Metaheuristics software

Most metaheuristics require considerable problem-specific design and tuning before they achieve world-class performance. Although a great deal of research effort is currently being invested in the development of more robust methods, the need for problem-specific design in order to obtain the best results has not inhibited the use of metaheuristics in general optimization software.

Nevertheless, several vendors of commercial general-purpose optimization software have included metaheuristics in their packages. Frontline Systems’ Risk Solver Platform and its derivatives, an extension of the Microsoft Excel solver, include a hybrid evolutionary solver. Tomlab/GENO is a package for static or dynamic, single- or multi-objective optimization based on a real-coded genetic algorithm. Both LINDO/LINGO and CPLEX include the relaxation induced neighborhood search (RINS) metaheuristic.

The COIN-OR library has several (open source) metaheuristics software packages: METSlib, an object oriented metaheuristics optimization framework, and Open Tabu Search (OTS), a framework for constructing tabu search algorithms. Besides these solvers for combinatorial optimization, most commercial simulation packages today include an optimization tool (Fu, 2002). Autostat, included in AutoMod and Simrunner, included in ProModel both use evolutionary algorithms. A variety of companies in the simulation industry, as well as general management service and consulting firms like Rockwell Software, Dassault Systemes, Flextronics, Halliburton, HP, Planview and CACI, employ Opttek Systems, Inc. software OptQuest, which uses tabu search and scatter search.

About the Authors

Kenneth Sorensen ( is a Research Professor of the Faculty of Applied Economics at Antwerp, and is founder and member of the coordinating team of EU/ME – the metaheuristics community, the largests online forum for metaheuristics researchers. Fred Glover ( is Distinguished Professor of the University of Colorado System and Chief Technology Officer of OptTek Systems, Inc.


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