Bio-Inspired Meta-Heuristic Algorithms: Sea-Horse Optimizer

Abstract

Optimization problems are problems that should identify the best solution to fulfill the constraints and max/min of the objective function. In general, metaheuristic algorithms, a community of nature-inspired approaches with specialized stochastic operators, have developed to address these difficult challenges. The development of computing technology indirectly encourages the development of several new algorithms. There has been a significant increase from the discovery of new metaheuristic methods in recent years to get better optimizations for non-linear and complex problems. Metaheuristic algorithm has the characteristics of a simple and optimal approach. In addition, metaheuristic algorithms are also durable and self-organized. Metaheuristic algorithms are computational intelligence paradigms especially used for sophisticated solving optimization problems. This paper proposes a novel swarm intelligence-based metaheuristic called as sea-horse optimizer (SHO), which is inspired by the movement, predation and breeding behaviors of sea horses in nature. The SHO is a method that duplicates the life of a Sea Horse in the ocean when it moves, looks for prey and breeds.