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"

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

Water flooding schemes introduced as part of redevelopment projects in mature fields are more often built on smart completions with multiple control valves (ICVs) in wells to be drilled. Decision processes for the implementation and operation of ICVs is supported by reservoir simulations to investigate the upside potential of technical production rates. The robustness of any presented solution is difficult to prove and requires workflows which integrate alternative geological scenarios for capturing uncertainties. In this work the employment of smart well technologies is modeled to investigate the potential for increasing o il recovery over the life time of a reservoir. Challenges exist on different levels. The number of control variables increases significantly as the number of wells, perforation sections per well and injection time intervals with varying injection constraints increases. Uncertainties related to different geological modeling concepts are taken into account for verifying the robustness of any optimized production scenario. The starting point for this paper is an ensemble of history matched simulation models. Ensemble-based production optimization including stochastic methods is applied for the optimization of water injection scenarios by individually adjusting ICVs. A novel concept for a time dependent target function is introduced. This reduces the number of control parameters adjusted at a time by focusing on incremental contributions to economic indicators. The workflow is applied to a complex reservoir model with production history. The optimization process is successfully improving economic indicators over the life time of the reservoir including a full risk evaluation based on alternative geological realizations.

The objective of this piece of research is to interpret and investigate systematically an observed brain functional phenomenon which associated with proceeding of e-learning processes. More specifically, this work addresses an interesting and challenging educational issue concerned with dynamical evaluation of elearning performance considering convergence (response) time. That's based on an interdisciplinary recent approach named as Artificial Neural Networks (ANNs) modeling. Which incorporate Nerophysiology, educational psychology, cognitive, and learning sciences. Herein, adopted application of neural modeling results in realistic dynamical measurements of e-learners' response time performance parameter. Initially, it considers time evolution of learners' experienced acquired intelligence level during proceeding of learning / training process. In the context of neurobiological details, the state of synaptic connectivity pattern (weight vector) inside e-learner's brain-at any time instant-supposed to be presented as timely varying dependent parameter. The varying modified synaptic state expected to lead to obtain stored experience spontaneously as learner's output (answer). Obviously, obtained responsive learner's output is a resulting action to any arbitrary external input stimulus (question). So, as the initial brain state of synaptic connectivity pattern (vector) considered as pre-intelligence level measured parameter. Actually, obtained elearner's answer is compatibly consistent with modified state of internal / stored experienced level of intelligence. In other words, dynamical changes of brain synaptic pattern (weight vector) modify adaptively convergence time of learning processes, so as to reach desired answer. Additionally, introduced research work is motivated by some obtained results for performance evaluation of some neural system models concerned with convergence time of learning process. Moreover, this paper considers interpretation of interrelations among some other interesting results obtained by a set of previously published educational models. The interpretational evaluation and analysis for introduced models results in some applicable studies at educational field as well as medically promising treatment of learning disabilities. Finally, an interesting comparative analogy between performances of ANNs modeling versus Ant Colony System (ACS) optimization presented at the end of this paper.

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Data assimilation techniques are on the verge of being employed in real field history matching processes in a production environment. In a previous publication on "Stochastic Optimization using EA and EnKF - A Comparison" (cf. Pajonk 2008) similarities between data assimilation techniques (EnKF) and stochastic optimizers (Evolutionary Algorithm - EA) were analyzed. Both algorithms are population based, they have similar implementation properties but differing optimization characteristics. A hybrid optimizer which couples an EnKF approach and the advantages of an Evolutionary Algorithm was introduced and applied to a synthetic test function. In this paper the formulation of a hybrid optimization approach with application to a history matching process is presented. Techniques are applied to the Brugge field simulation model which was taken from a recent SPE benchmark study. Production data is assimilated via a continuous update of 3D porosity and permeability fields. Global parameter uncertainties are included in a parameter estimation process guided by an evolutionary optimization method. In this paper we will concentrate on an Evolution Strategy with local and global search properties. It is shown that an EnKF workflow can be effectively coupled to other stochastic optimization schemes with complimentary optimization features. The EnKF formulation reduces a non-linear optimization problem in a large parameter space to a statistical optimization problem in ensemble space. An Evolution Strategy (ES) gradually modifies individual parameters and can be applied to mixed-integer parameter types. The case example shows that an EnKF ensemble can be combined with a population of individual realizations from a generational update scheme using an Evolution Strategy. Benefits are seen in alternative performance properties and the use of mixed-integer parameter types. This paper will include the first example of a hybrid EnKF-ES approach with application to reservoir simulation. Practical implications for history matching processes with mixed-integer parameter types which have not been used in a standard EnKF approach are discussed. Introduction In recent years more and more attention has been given to workflows for uncertainty assessment in reservoir management. It is generally accepted that any model reliably predicting future quantities should be able to reproduce known history data. This requires a model validation process (History Matching) which is traditionally cumbersome and time consuming. Most optimization methods described in the literature that have relevance for history matching in reservoir simulation use an objective function definition based on the overall simulation period. This applies to gradient techniques, evolutionary algorithms or combinations of experimental design and proxy modeling techniques, i.e., methods which are commonly used in modern history matching and uncertainty quantification workflows in the oil and gas industry. The integration of a sequential data assimilation process is not included as an integral part of such optimization methods. Frequent model updates would also incur high computational costs since existing concepts of an error function or objective function require computation of the entire simulation period. The Ensemble Kalman Filter method (EnKF) is a data assimilation technique that was developed for use in complex and highly non-linear simulation models (Evensen 1994). The EnKF technique was first applied to reservoir simulation in order to estimate lesser known rock properties as part of a history matching process (Nævdal 2003).

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