The main idea to author this article is to "Popularize Artificial Human Optimization Field like never before by showing an Overview of this new field". This idea can be divided into following sub-ideas: 1) To show the definition of "Artificial Human Optimization Field (AHO Field)" 2) To show difficulty level of creating new algorithms under AHO field 3) To show 30+ titles of papers published under AHO field 4) To show names of 65+ authors who worked under AHO Field 5) To show best negative reviews obtained for work under AHO Field 6) To show best positive reviews obtained for work under AHO Field 7) To show feedback given by an expert for work under AHO Field 8) To show "Hassan Satish Particle Swarm Optimization (HSPSO)". This is latest work under AHO Field 9) To show contribution of Satish Gajawada and co-authors to this new Field 10) To show surprising results obtained after implementing AHO algorithms 11) To show you "Future of Artificial Human Optimization Field"

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Lines 4 to 11 in the below text helps in maintaining particle best and global best. Then the velocity is updated by rule shown in line no. 14. Line 15 updates position of i th particle. Line 19 increments the number of iterations and

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a semi-supervised projected clustering method based on DE. In this method DE optimizes a hybrid cluster validation index. Subspace Clustering Quality Estimate index (SCQE index) is used for internal cluster validation and Gini index gain is used for external cluster validation in the proposed hybrid cluster validation index. Proposed method is applied on Wisconsin breast cancer dataset.