Which is the best metaheuristic algorithm?

Which is the best metaheuristic algorithm?

Most widely known Meta-heuristic algorithms are Genetic algorithm (GA), simulated annealing (SA) and Tabu search (TS).

How do metaheuristics work?

Metaheuristics are strategies that guide the search process. The goal is to efficiently explore the search space in order to find near–optimal solutions. Techniques which constitute metaheuristic algorithms range from simple local search procedures to complex learning processes.

What is the difference between heuristics and metaheuristics?

So, heuristics are often problem-dependent, that is, you define an heuristic for a given problem. Metaheuristics are problem-independent techniques that can be applied to a broad range of problems. An heuristic is, for example, choosing a random element for pivoting in Quicksort.

What is meant by metaheuristic algorithm?

Metaheuristic algorithms are computational intelligence paradigms especially used for sophisticated solving optimization problems. This chapter aims to review of all metaheuristics related issues.

Is genetic algorithm heuristic and metaheuristic?

So, What is Genetic Algorithm (GA)? GA is a population-based metaheuristic developed by John Holland in the 1970s. GA uses techniques inspired from nature, more specifically evolution, to find an optimal or near-optimal solution towards a problem.

Which algorithm is best for vehicle routing problem?

Metaheuristic algorithms are selected to solve the vehicle routing problem, where GA is implemented as our primary metaheuristic algorithm.

Why are metaheuristic methods needed?

A metaheuristic method helps in solving the optimization problem. Problems in optimization can be found in many daily life aspects. The kinds of the metaheuristic method are various which are ant colony optimization (ACO), genetic algorithm (GA), and particle swarm optimization (PSO).

What is the main difference between an algorithm and a heuristic?

An algorithm is a step-wise procedure for solving a specific problem in a finite number of steps. The result (output) of an algorithm is predictable and reproducible given the same parameters (input). A heuristic is an educated guess which serves as a guide for subsequent explorations.

What is the need and use of metaheuristics?

Metaheuristics define algorithmic frameworks that can be applied to solve such problems in an approximate way, by combining constructive methods with local and population-based search strategies, as well as strategies for escaping local optima.

Is genetic algorithm metaheuristic?

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).

Why genetic algorithm is metaheuristic?

GA is a population-based metaheuristic developed by John Holland in the 1970s. GA uses techniques inspired from nature, more specifically evolution, to find an optimal or near-optimal solution towards a problem. It applies evolution concepts such as reproduction and survival of the fittest to solve a problem.

What is the difference between TSP and VRP?

TSP considers a single vehicle visiting multiple customer locations before returning to the depot, and we want to minimize the total travel time or vehicle distance. VRP differs from TSP because VRP can generate multiple routes to pass through all customer locations 2 .

Why is VRP important?

The objective function of a VRP can be very different depending on the particular application of the result but a few of the more common objectives are: Minimize the global transportation cost based on the global distance travelled as well as the fixed costs associated with the used vehicles and drivers.

Is genetic algorithm heuristic or metaheuristic?

Which approaches used in metaheuristic optimization techniques?

Contents

• 3.1 Simulated Annealing.
• 3.2 Genetic Algorithms.
• 3.3 Differential Evolution.
• 3.4 Ant Colony Optimization.
• 3.5 Bee Algorithms.
• 3.6 Particle Swarm Optimization.
• 3.7 Tabu Search.
• 3.8 Harmony Search.

What type of algorithm is simulated annealing?

Simulated Annealing is a stochastic global search optimization algorithm. The algorithm is inspired by annealing in metallurgy where metal is heated to a high temperature quickly, then cooled slowly, which increases its strength and makes it easier to work with.

What is simulated annealing explain its algorithm?

The simulated annealing algorithm is an optimization method which mimics the slow cooling of metals, which is characterized by a progressive reduction in the atomic movements that reduce the density of lattice defects until a lowest-energy state is reached [143].