COMPARATIVE ANALYSIS OF SWARM INTELLIGENCE TECHNIQUES

Abstract

For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multiagent systems by taking inspiration from the collective behaviors social insects and other animal societies. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the comparative analysis of most successful methods of optimization techniques inspired by Swarm Intelligence (SI): Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Cuckoo search (CS). In this paper, an elaborative comparative analysis is carried out and plans to endow these algorithms with fitness sharing, aiming to investigate whether this helps in improving performance can be implemented in the evolutionary algorithms.