Swarm Intelligence

4Algorithm of Swarm Intelligence

Abstract

Evolutionary programming through contemporary gray wolf optimization and several swarm optimization techniques was introduced in the early 1960s. The potential of all these methods to tackle various optimization issues has been shown. This chapter thoroughly examines well-known optimization methods. Studies carried out with 30 well-known benchmarking functions are briefly described and compared with selected plans. Next, several statistical studies are presented that confirm significant results. The outcomes demonstrate the overall benefit of differential development (DE) and carefully contrast with other proposed techniques followed by particle swarm optimization (PSO).

Keywords: Ant behavior, shortest path, pheromone, optimal solution, bee summing, model-based search, optimization

4.1 Introduction

Like bees and termites, ants are classified as social animals because they live and operate in well-organized colonies or groups of the same species. Ants form small to large colonies with populations ranging from a few hundred to thousands of individuals, mostly sterile females forming several classes. Because of their social nature, these colonies are referred to as select different. Ants are almost invisible insects that successfully seek food. It’s fascinating to learn how this species, although essentially individuals, recognizes the value of cooperation in determining the quickest path connecting their nest and providing nutrients ...