Border Collie Optimization

Abstract

In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

Border Collie optimization algorithm‐based node clustering technique in vehicular ad hoc networks

“…fd pos (t + 1) = V fd (t + 1) × Rt fd (t + 1) + 1 2 Acc fd (t + 1) × (Rt fd (t + 1)) 2 (17) Furthermore, the acceleration of the cluster heads (Acc dg (t + 1)) and its associated cluster members (Acc sh (t + 1)) is updated based on Equations ( 18) and (19).…”

“…The population‐based algorithms are generally focused on randomization and possess two vital stages such as exploration and exploitation for determining optimal results 17,18 . A Border Collie optimization algorithm (BCOA) was developed based on the inspiration derived from the sheep herding styles of Border Collie dogs 19 . The herding styles of Border Collie dogs are unique and they track the sheep by leading from the front and on the left and right directions.…”

“…17,18 A Border Collie optimization algorithm (BCOA) was developed based on the inspiration derived from the sheep herding styles of Border Collie dogs. 19 The herding styles of Border Collie dogs are unique and they track the sheep by leading from the front and on the left and right directions. However, the number of right and left dogs depends on the number of dogs that need to be tracked and eyed.…”

Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems

“…Border Collie is one of the most smartest breeds of dogs and has unique herding style. By referencing the sheep herding styles of Border Collie dogs, Dutta et al introduced Border Collie Optimization (BCO) algorithm [32].…”

Modified Particle Swarm Optimization With Effective Guides

“…For swarm intelligence algorithms, their search mechanisms are motivated by the collective behaviors of decentralized and self-organized systems such as animals and insects. These include: particle swarm optimization [10], ant colony optimization [11], artificial bee colony [12], grey wolf optimizer [13], grasshopper optimization [14] and border collie optimization [15]. The working mechanisms of human-based algorithms are motivated by different human activities such as thinking, learning, talking and etc.…”