Metaheuristic
Introduction
A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. The term is also used to refer to a problem-specific implementation of a metaheuristic optimization algorithm. Metaheuristics are not problem-specific and can be used to solve a wide range of optimization problems. They are typically used when no specific classical algorithmic approach can be applied.
Overview
Metaheuristics are strategies that guide the search process to efficiently explore the search space in order to find near-optimal solutions. They are designed to solve complex optimization problems where the search space is large, complex, or poorly understood. Metaheuristics can be applied to any problem that involves the search for an optimal or near-optimal solution.
Characteristics
Metaheuristics possess certain characteristics that distinguish them from other optimization algorithms. They are generally non-deterministic, meaning that they incorporate some form of randomness in their search process. They are also iterative, meaning that they progressively improve the quality of a solution by iteratively refining it. Furthermore, they are population-based, meaning that they work with a set of solutions rather than a single solution.
Classification
Metaheuristics can be classified into two main categories: single-solution metaheuristics and population-based metaheuristics. Single-solution metaheuristics, such as simulated annealing and tabu search, start with a single solution and iteratively improve it. On the other hand, population-based metaheuristics, such as genetic algorithms and particle swarm optimization, work with a set of solutions and iteratively improve it.
Single-Solution Metaheuristics
Single-solution metaheuristics are characterized by the iterative improvement of a single solution. They typically employ a local search strategy, where the neighborhood of the current solution is explored to find a better solution. The process is repeated until a stopping criterion is met.
Simulated Annealing
Simulated annealing is a probabilistic technique for approximating the global optimum of a given function. It is often used when the search space is discrete. The name and inspiration come from annealing in metallurgy, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects.
Tabu Search
Tabu search is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of tabu search is its use of adaptive memory, which creates a more flexible search behavior. Tabu search uses a tabu list of solutions, which are forbidden to be revisited, to guide the search.
Population-Based Metaheuristics
Population-based metaheuristics are characterized by the iterative improvement of a set of solutions. They typically employ a global search strategy, where the entire search space is explored to find the global optimum. The process is repeated until a stopping criterion is met.
Genetic Algorithms
Genetic algorithms are inspired by the process of natural selection and genetics. They are used to find approximate solutions to optimization and search problems. Genetic algorithms operate on a population of potential solutions applying the principle of survival of the fittest to produce better approximations to a solution.
Particle Swarm Optimization
Particle swarm optimization is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It is inspired by the social behavior of bird flocking or fish schooling.
Applications
Metaheuristics are widely used in various fields such as operations research, computer science, engineering, economics, and bioinformatics. They are used to solve complex optimization problems that cannot be solved by traditional optimization methods. Examples of such problems include scheduling, routing, and allocation problems.