Research Area: Metaheuristic Computing
In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems.
Keywords:
Particle Swarm
Artificial Bee Colony
Firefly Algorithm
Differential Evolution
Genetic Algorithm
Crow Search
Dragonfly
Moth-flame
Sine Cosine Algorithm
selfish herd optimizer
predation risk
exploration
exploitation
population size
Author(s) Name: Fernando Fausto, Erik Cuevas, Arturo Valdivia, Adrián González
Journal name: Biosystems
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.biosystems.2017.07.010
Volume Information: Volume 160, Pages 39-55
