What Is The Grey Wolf Optimizer?
Artificial immune systems (AIS) concerns the usage of abstract structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology. AIS are a sub-field of biologically inspired computing, and natural computation, with interests in Machine Learning and belonging to the broader field of Artificial Intelligence.
5.2.9 Grey Wolf Optimizer
The Grey wolf optimizer (GWO) algorithm mimics the leadership hierarchy and hunting mechanism of gray wolves in nature proposed by Mirjalili et al. in 2014. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership …show more content…
The position of each agent is represented by XY-axis position and the velocity (displacement vector) is expressed by vx (the velocity of X-axis) and vy (the velocity of Y-axis). Particle are change their searching point from Si to S i +1 by adding their updated velocity Vi with current position Si. Each particle tries to modify its current position and velocity according to the distance between its current position Si and V pbest, and the distance between its current position Si and V gbest . As stated before, PSO simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are randomly searching food in an area. There is only one piece of food in the area being searched. All the birds do not know where the food is?, but they know how far the food is in each iteration. So what's the best strategy to find the food? The effective one is to follow the bird which is nearest to the food. PSO learned from the scenario and used it to solve the optimization problems. In PSO, each single solution is a "bird" in the search space. We call it "particle". All of particles have fitness values which are evaluated by the fitness function to be optimized, and have velocities which direct the flying of the particles. The particles fly
5.2.9 Grey Wolf Optimizer
The Grey wolf optimizer (GWO) algorithm mimics the leadership hierarchy and hunting mechanism of gray wolves in nature proposed by Mirjalili et al. in 2014. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership …show more content…
The position of each agent is represented by XY-axis position and the velocity (displacement vector) is expressed by vx (the velocity of X-axis) and vy (the velocity of Y-axis). Particle are change their searching point from Si to S i +1 by adding their updated velocity Vi with current position Si. Each particle tries to modify its current position and velocity according to the distance between its current position Si and V pbest, and the distance between its current position Si and V gbest . As stated before, PSO simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are randomly searching food in an area. There is only one piece of food in the area being searched. All the birds do not know where the food is?, but they know how far the food is in each iteration. So what's the best strategy to find the food? The effective one is to follow the bird which is nearest to the food. PSO learned from the scenario and used it to solve the optimization problems. In PSO, each single solution is a "bird" in the search space. We call it "particle". All of particles have fitness values which are evaluated by the fitness function to be optimized, and have velocities which direct the flying of the particles. The particles fly