Contact: Kalyanmoy Deb: kdeb@egr.msu.edu
"Optimization is method of arriving at high-performing solutions to a problem through a systematic evaluation of solutions in the search space. They honor the supplied constraint functions and iteratively approach the optimal solutions minimizing or maximizing one of more objective functions. Despite over 70 years of research and application, the scope and practical issues related to applying an optimization algorithm to a problem are not properly understood or laid out. It is no denying that no single optimization algorithm is best for all problems, requiring one to develop a customized algorithm for a problem class.
Our principle has been to utilize key problem information associated a problem class and develop a customized algorithm to solve the problem routinely. Our optimization research is distinguished from others is that our approaches are practical from a computational point of view. We also specialize in handling multiple conflicting objectives, involving multi-objective optimization and ensuing decision-making aspects."
Projects
Crop Yield Simulation Optimization Using Precision Irrigation and Subsurface Water Retention Technology
Maximizing crop production with minimal resources, such as water and energy is the primary focus of sustainable agriculture. Subsurface water retention technol-ogy (SWRT) is a stable approach that preserves water in sandy soils using water saving membranes. An optimal use of SWRT depends on its shape, location and other factors. In order to predict crop yield for different irrigation schedule, we require at least two computational processes: (i) a crop growth modeling pro-cess and (ii) a water and nutrient permeation process through soil to the root system.
In this project, we proposed a computationally fast approach that utilizes HYDRUS-2D software for water and nutrient flow simulation and DSSAT crop simulation software with an evolutionary multi-objective optimization (EMO) procedure in a coordinated manner to minimize water utilization and maximize crop production. Our proposed method consists of training one-dimensional crop model (DSSAT) on data generated by two dimensional model calibrates and validates (HYDRUS-2D), that accounts for water accumulation in the SWRT membranes. Then we used DSSAT model to find the best irrigation schedules for maximizing crop yield with the highest plant water use efficiency [1, 2] using for the EMO methodology. The optimization procedure minimizes water usage with the help of rainfall water and increases corn yield prediction as much as six times compare to a non- optimized and random irrigation schedule without any SWRT membrane.
Our framework also demonstrates an integration of latest computing software and hardware technologies synergistically to facilitate better crop production with minimal water requirement.
Triple Bottom Line Land Use Management of a New Zealand Farm
A land use many-objective optimization problem for a 1500-ha farm with 315 paddocks was formulated with 14 objectives (maximizing sawlog production, pulpwood production, milksolids, beef, sheep meat, wool, carbon sequestration, water production, income and Earnings Before Interest and Tax; and minimizing costs, nitrate leaching, phosphorus loss and sedimentation). This was solved using a modified Reference-point-based Non-dominated Sorting Genetic Algorithm II augmented by simulated epigenetic operations. The search space had complex variable interactions and was based on economic data and several interoperating simulation models. The solution was an approximation of a Hyperspace Pareto Frontier (HPF), where each non-dominated trade-off point represented a set of land-use management actions taken within a 10-year period and their related management options, spanning a planning period of 50 years.
A trade-off analysis was achieved using Hyper-Radial Visualization (HRV) by collapsing the HPF into a 2-D visualization capability through an interactive virtual reality (VR)-based method, thereby facilitating intuitive selection of a sound compromise solution dictated by the decision makers’ preferences under uncertainty conditions. Four scenarios of the HRV were considered emphasizing economic, sedimentation and nitrate leaching aspects—giving rise to a triple bottomline (i.e. the economic, environmental and social complex, where the social aspect is represented by the preferences of the various stakeholders).
Highlights of the proposed approach are the development of an innovative epigenetics-based multi-objective optimizer,
uncertainty incorporation in the search space data and decision making on a multi-dimensional space through a VR-simulation-based visual steering process controlled at its core by a multi-criterion decision making-based process. This approach has widespread applicability to many other ‘wicked’ societal problem-solving tasks.
