Abstract-This paper presents a comparative analysis of Modified Social Emotional Optimization Algorithm and Particle Swarm Optimization techniques. Social Emotional Optimization

Algorithm is a population based stochastic optimization technique in which the impact of human beings ’ emotional factors on decision-making practices is emphasized where as Swarm intelligence (SI) techniques like Genetic algorithm, Ant Colony Optimization and Particle Swarm Optimization are based on swarm behaviour. Although GA [1], PSO [2][3] and ACO[9] algorithms have lot of advantages but they simply simulate group behaviours and animal foraging. Social Emotional Optimization Algorithm (SEOA)[5] is a new swarm intelligent technique, that simulates human social behaviour. In SEOA, each individual represents one virtual person who communicates through cooperation and competition in the society. This paper focuses on the comparative analysis of Modified Social Emotional Optimization Algorithm in comparison with most successful method of optimization techniques inspired by Swarm Intelligence (SI) : Particle Swarm Optimization (PSO) and a novel swarm intelligent population-based optimization algorithm Social Emotional Optimization (SEOA). An elaborate comparative analysis is carried out to endow these algorithms, aiming to investigate whether the Modified Social Emotional Optimization Algorithm improves performance which can be implemented in many areas