Metaheuristic optimization frameworks: a survey and benchmarking

@article{Parejo2012MetaheuristicOF,
  title={Metaheuristic optimization frameworks: a survey and benchmarking},
  author={Jos{\'e} Antonio Parejo and Antonio Ruiz Cort{\'e}s and Sebasti{\'a}n Lozano and Pablo Fern{\'a}ndez},
  journal={Soft Computing},
  year={2012},
  volume={16},
  pages={527-561}
}
This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric… 
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143 Citations
Highly Influential Citations
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Background Citations
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Methods Citations
16
Results Citations
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143 Citations

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A set of tools to support the selection, configuration and evaluation of metaheuristic-based applications is presented to reduce the cost of applying metaheuristics for solving optimization problems.
How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?
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The effect of a raised evaluation budget on overall performance, mean convergence, and population diversity of selected swarm algorithms and IEEE CEC competition winners is examined.
JCLEC-MO: A Java suite for solving many-objective optimization engineering problems
An Extensible JCLEC-based Solution for the Implementation of Multi-Objective Evolutionary Algorithms
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A number of relevant features serving to satisfy the requirements demanded by MOO nowadays are identified, and a solution is proposed, called JcleC-MOEA, on the basis of the JCLEC framework, designed with a twofold purpose: reusing all the features already given by a mature framework like J CLEC and extending it to enable new developments more flexibly than current alternatives.
Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
TLDR
This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics that may support the progress of the field and increase the maturity of meta heuristics as problem solvers analogous to other machine learning algorithms.
Hybrid metaheuristics and multi-agent systems for solving optimization problems: A review of frameworks and a comparative analysis
Metaheuristics "In the Large"
Nature inspired meta heuristic algorithms for optimization problems
TLDR
This work’s major contribution is on building a hyper heuristics approach from a meta-heuristic algorithm for any general problem domain.
Nature inspired meta heuristic algorithms for optimization problems
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.
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