DLHO-:An Enhanced Version of Harris Hawks Optimization By Dimension Learning-Based Hunting For Breast Cancer And Other Serious Diseases Detection

In the bio-medical science various diseases are most serious and are prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. Swarm intelligence techniques play an important role for the solution of these types of diseases. However due to some shortcomings of these methods such as slow convergence, premature convergence and weak local avoidance etc various complexities are faced. In this study, An enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical datasets, it is known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To test the performance of the proposed technique different set of experiments have been performed and results are compared with various recent metaheuristics. First, the performance of optimizer is analysed by using 29-CEC -2017 test suites. Second, to demonstrate the robustness of the proposed technique results have been taken on five bio-medical datasets such as XOR, Balloon, Iris, Breast Cancer and Heart. All the results are in the favour of proposed technique.