Cluster optimisation in information retrieval using self-exploration-based PSO

Abstract

Self-exploration capability is an important and necessary factor in all social communities where individual assumes to have their own intelligence. Macro social influencing factors are responsible for decision nature taken by an individual, whereas self-exploration process can be considered as a refinement of that decision by use of the cognitive capability to explore a number of surrounding possibilities. The mathematical model corresponding to the individual self-exploration process can be expressed with the help of the chaotic search method. In this paper, chaotic search-based self-exploration has integrated with social influenced-based particle swarm optimisation (PSO) to represent better computational model so that the complex optimisation problem could solve more efficiently. Two different levels of self-exploration called intrinsic cascade self-exploration and extrinsic cascade self-exploration have applied in association with PSO. This paper has applied the proposed concept to cluster documents data in the area of information retrieval and to achieve the global solutions for high dimensional numerical optimisation problems.

Evolutionary-based method for risk stratification of diabetic patients

“…In the past years, researchers have focused on applying metaheuristics to solve various optimisation problems due to their ability to provide a good solution in an acceptable execution time. For example, Prakasha and Raju (2016) apply metaheuristic algorithms for cluster optimisation in information retrieval, while Ramadan (2017) applies metaheuristics for solving the energy topology control problem. Mahmoodabadi et al (2015) use metaheuristics for mathematical optimisation, while Aza (2016) applies metaheuristics for neonatal jaundice diagnosis.…”