A highly effective algorithmic framework for solving the most complex optimization problems

A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, 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.

This paper explains the concept of metaheuristics, describes the key differences/pros and cons from more traditional (exact) methods of mixed-integer optimization (e.g., branch and bound and dynamic programming), outlines the different types of metaheuristics (local search, constructive, population-based, and hybrid), and discusses the combined use of metaheuristics with exact methods to solve many complex, real-world problems. In addition, this paper describes the use of metaheuristics to solve a variety of optimization problems, including continuous, multi-objective, and stochastic. The paper concludes with a look at research efforts in the area of metaheuristics.

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