Influence expansion issue is to locate a lot of seeds in informal communities with the end goal that the course influence is boosted. Conventional models expect that all hubs are eager to spread the influence once they are influenced, and they overlook the divergence among influence and benefit of an item. Suggestion frameworks have gotten significant consideration as of late. Be that as it may, most research has been centered around improving the presentation of collective separating procedures. Interpersonal organizations, crucially, give us additional data on individuals' inclinations, and ought to be considered and conveyed to improve the nature of suggestions. This paper displays a hybrid algorithm joining two heuristic optimization methods, ACO (Ant Colnoly Optimization) and HMFA (Hybrid Modified Firefly Algorithm) for amplifying Social system clients influence level.. The proposed algorithm coordinates the benefits of both ACO and HMFA, where the algorithm is introduced by a lot of arbitrary ants that is meandering through the inquiry space. During this wandering a development of these ants is performed by coordinating ACO and HMFA, where HMFA fills in as a neighborhood search to refine the positions found by the ants. Then again, the exhibition of HMFA is improved by lessening the randomization parameter with the goal that it diminishes slowly as the optima are drawing nearer. The examinations of numerical outcomes demonstrate that there is an extent of research in hybridizing swarm insight techniques to tackle troublesome persistent optimization issues and this hybrid ACO–HMFA is a promising and profitable device to take care of unconstrained nonlinear optimization issues. A cautious perception will uncover the accompanying advantages of the proposed optimization algorithm.
A non-Secant quasi-Newton Method for Unconstrained Nonlinear Optimization
