Comparative Analysis of Modified Social Emotional Optimization Algorithm & Particle Swarm Optimization Techniques
Swapna Devi1, Shanu Singla2
1Dr. Swapna Devi, Department of ECE, National Institute of Technical Teachers’ Training and Research, Chandigarh, India.
2Shanu Singla, Department of ECE, J.P.N.C.E., Mahbubnagar, A.P., India.
Manuscript received on 15 November 2012 | Revised Manuscript received on 25 November 2012 | Manuscript Published on 30 November 2012 | PP: 82-84 | Volume-1 Issue-6, November 2012 | Retrieval Number: E0337101612/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Social Emotional Optimization, Particle Swarm Optimization and Swarm intelligence.
Scope of the Article: Particle Swarm