What is alpha and beta in ant colony optimization?

An ACO is one of the best methods to find the shortest path. ACO uses parameters called alpha, beta (also called control parameters) and evaporation rate, to find the shortest path on probability basis. We have tried to optimize these parameters to find the path of minimum length and cost.

Is ant colony optimization a heuristic?

The use of heuristic information is crucial for good performance of ant colony optimization (ACO) algorithms. The use of heuristic information can guide the artificial ants towards the most promising solutions.

What is the advantage of ant colony optimization over genetic algorithm?

They have an advantage over simulated annealing and genetic algorithm approaches of similar problems when the graph may change dynamically; the ant colony algorithm can be run continuously and adapt to changes in real time.

What is ant colony optimization PDF?

Ant Colony Optimization (ACO) is a population-based, general search technique for the solution of difficult combinatorial problems which is inspired by the pheromone trail laying behavior of real ant colonies.

What is cuckoo search optimization?

The Cuckoo Search algorithm is a recently developed meta-heuristic optimization algorithm, which is used for solving optimization problems. This is a nature-inspired metaheuristic algorithm, which is based on the brood parasitism of some cuckoo species, along with Levy flights random walks.

Is ant colony an evolutionary algorithm?

So, GAs and ACO are evolutionary algorithms inspired by different nature phenomena.

How ant colony optimization is implemented for solving any problem?

An artificial ant is made for finding the optimal solution. In the first step of solving a problem, each ant generates a solution. In the second step, paths found by different ants are compared. And in the third step, paths value or pheromone is updated.

What is whale optimization algorithm?

The Whale Optimization Algorithm (WOA) is a new optimization technique for solving optimization problems. This algorithm includes three operators to simulate the search for prey, encircling prey, and bubble-net foraging behavior of humpback whales. This is the source codes of the paper: S. Mirjalili, A.

Is Ant Colony Optimization a genetic algorithm?

In a way, it can be considered a shortcut. Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Soccer Games Optimization (SGO), are some examples of heuristic method algorithms for optimization [2][3].

Who proposed ant colony optimization?

Marco Dorigo
12.2 Overview of Ant Colony Optimization ACO was initially proposed by Marco Dorigo in his Ph. D. thesis in the early 90s [31] aiming to solve the optimal path problem in a graph.

What is Pbest and Gbest in PSO?

Each particle have to maintain its positions pbest known as local best position and the Gbest known as global best position among all the particles. Following equations are used to update the position and velocity of the particle.

What is the advantage of genetic algorithm?

Advantages of genetic algorithm It has excellent parallel capabilities. It can optimize various problems such as discrete functions, multi-objective problems, and continuous functions. It provides answers that improve over time. A genetic algorithm does not need derivative information.