Metaheuristic methods
Introduction
Metaheuristic methods are high-level problem-solving strategies designed to find, generate, or select a heuristic that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. These methods are a subset of heuristic optimization methods and have been widely used in many complex optimization problems in various fields such as engineering, economics, and logistics.
Overview
Metaheuristic methods can be classified into two main categories: single-solution based and population-based. Single-solution based 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, which evolve over time.
Single-Solution Based Metaheuristics
Single-solution based metaheuristics are strategies that start with a single solution and iteratively improve it. They are typically easy to implement and require less computational resources compared to population-based metaheuristics.
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. The main components of tabu search are its use of adaptive memory and responsive exploration, which make it flexible to apply to a wide range of optimization problems.
Population-Based Metaheuristics
Population-based metaheuristics are strategies that work with a set of solutions, which evolve over time. They are typically more complex to implement and require more computational resources compared to single-solution based metaheuristics. However, they often provide better solutions and are more robust to changes in the problem.
Genetic Algorithms
Genetic algorithms are a type of evolutionary algorithm that mimic the process of natural selection. They operate on a population of potential solutions using the principles of evolution, such as mutation, crossover, and selection.
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
Metaheuristic methods have been widely used in many complex optimization problems in various fields such as engineering, economics, and logistics. Some of the applications include scheduling problems, vehicle routing problems, and machine learning.