A survey on metaheuristics for stochastic combinatorial optimization

@article{Bianchi2008ASO,
  title={A survey on metaheuristics for stochastic combinatorial optimization},
  author={L. Bianchi and M. Dorigo and L. Gambardella and W. Gutjahr},
  journal={Natural Computing},
  year={2008},
  volume={8},
  pages={239-287}
}
Metaheuristics are general algorithmic frameworks, often nature-inspired, designed to solve complex optimization problems, and they are a growing research area since a few decades. In recent years, metaheuristics are emerging as successful alternatives to more classical approaches also for solving optimization problems that include in their mathematical formulation uncertain, stochastic, and dynamic information. In this paper metaheuristics such as Ant Colony Optimization, Evolutionary… Expand
View on Springer
doc.rero.ch
528 Citations
Highly Influential Citations
24
Background Citations
185
Methods Citations
66
Results Citations
1

528 Citations

Citation Type

Citation Type

A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems
TLDR
A general methodology that allows for extending metaheuristics through simulation to solve stochastic COPs and helps modelers for dealing with real-life uncertainty in a natural way by integrating simulation (in any of its variants) into a metaheuristic-driven framework is described. Expand
A survey on new generation metaheuristic algorithms
TLDR
In this survey, fourteen new and outstanding metaheuristics that have been introduced for the last twenty years other than the classical ones such as genetic, particle swarm, and tabu search are distinguished. Expand
Metaheuristics 1
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, To… Expand
A Walk into Metaheuristics for Engineering Optimization: Principles, Methods and Recent Trends
TLDR
The principles and the state-of-the-art of metaheuristic methods for engineering optimization, both the classic and emerging approaches to optimization using metaheuristics are reviewed and analyzed. Expand
Comparative study of population-based metaheuristic methods in global optimization
TLDR
This thesis studies so-called population-based metaheuristics, especially Nature-inspired evolutionary algorithms and swarm intelligence, and studies their capabilities of solving a difficult real-world optimization problem, the Lennard-Jones cluster structure problem. Expand
Metaheuristics for Stochastic Problems
A large number of publications have been written about metaheuristics and their use in solving deterministic optimization problems. As metaheuristic techniques become more widespread in the… Expand
Recent Development of Metaheuristics for Clustering
TLDR
To evaluate the performance of eight novel metaheuristics, they are applied to a well-known combinatorial optimization problem, data clustering, and the results are analyzed and discussed. Expand
Why Simheuristics? Benefits, Limitations, and Best Practices When Combining Metaheuristics with Simulation
TLDR
This paper argues that approaches combining simulation with meta heuristics not only constitute a natural extension of metaheuristics, but also should be considered as a “first resort” method when dealing with large-scale stochastic optimization problems, which constitute most realistic problems in industry and business. Expand
Metaheuristic Techniques
Most real-world search and optimization problems involve complexities such as non-convexity, nonlinearities, discontinuities, mixed nature of variables, multiple disciplines and large dimensionality,… Expand
Metaheuristic profiling to assess performance of hybrid evolutionary optimization algorithms applied to complex wellbore trajectories
Abstract Metaheuristic profiling is proposed as an effective technique with which to evaluate the relative contributions of the metaheuristic components of hybrid evolutionary optimization algorithms… Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 187 REFERENCES
Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization
In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the… Expand
Metaheuristics: Progress in Complex Systems Optimization
TLDR
A delineation of general Metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas are provided. Expand
Hybrid Metaheuristics for the Vehicle Routing Problem with Stochastic Demands
TLDR
This article analyzes the performance of metaheuristics on the vehicle routing problem with stochastic demands (VRPSD) and explores the hybridization of the metaheuristic by means of two objective functions which are surrogate measures of the exact solution quality. Expand
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
TLDR
A survey of the nowadays most important metaheuristics from a conceptual point of view and introduces a framework, that is called the I&D frame, in order to put different intensification and diversification components into relation with each other. Expand
Ant colony optimization theory: A survey
TLDR
A survey on theoretical results on ant colony optimization, which highlights some open questions with a certain interest of being solved in the near future and discusses relations between ant colonies optimization algorithms and other approximate methods for optimization. Expand
A Converging ACO Algorithm for Stochastic Combinatorial Optimization
TLDR
It is shown that on rather mild conditions, including that of linear increment of the sample size, the algorithm converges with probability one to the globally optimal solution of the stochastic combinatorial optimization problem, and can usually be recommended for practical application in an unchanged form, i.e., with the ”theoretical” parameter schedule. Expand
S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty
TLDR
A general-purpose, simulation-based algorithm S-ACO for solving stochastic combinatorial optimization problems by means of the ant colony optimization (ACO) paradigm outperforms the SSA approach on the considered test instances. Expand
Variable neighborhood search: Principles and applications
TLDR
A basic scheme for variable neighborhood search, which can easily be implemented using any local search algorithm as a subroutine, and various ways to use VNS in graph theory, i.e., to suggest, disprove or give hints on how to prove conjectures, are discussed. Expand
Evolutionary optimization in uncertain environments-a survey
  • Yaochu Jin, J. Branke
  • Mathematics, Computer Science
  • IEEE Transactions on Evolutionary Computation
  • 2005
TLDR
This paper attempts to provide a comprehensive overview of the related work within a unified framework on addressing different uncertainties in evolutionary computation, which has been scattered in a variety of research areas. Expand
Ant colony optimization: Introduction and recent trends
TLDR
This work deals with the biological inspiration of ant colony optimization algorithms and shows how this biological inspiration can be transfered into an algorithm for discrete optimization, and presents some of the nowadays best-performing ant colonies optimization variants. Expand
...
1
2
3
4
5
...