Analysis of Search Space in the Domain of Swarm Intelligence

The formulation and analysis of complex problems to get optimum solution is an emerging art which inspired new perspective in optimization world. Derived from biological system swarm intelligence algorithm attract many researcher due to its simplicity and adaptable nature with real-life problem. But swarm algorithms suffer from its search process. Higher rate of exploitation may trap to pre-mature solution and higher rate of exploration may slow down the process or may not true solution. In this paper, we present detailed analysis and sorting of search strategies based on its property and structure to get optimum of exploration and exploitation for swarm algorithms. In our study, we cluster majority of strategies in to information sharing and change in structure. In first part, sharing of information is categorized by population initialization, population update and population control. In second part, change in structure is categorized by modification in governing equation, hybridization and new topology. At last, the analysis attempts to provide understanding for different strategies, current issues and future research for expert and researcher.