Evolutionary Multi-objective Optimization Methodologies Research Articles

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 technology (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 process and (ii) a water and nutrient permeation process through soil to the root system. Validation of software parameters to suit properties of specific field becomes increasingly hard since they involve a coordination with field data and coordination between two software. In this paper, we propose 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 yield prediction. 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 (Tambussi et al., 2007; Blum, 2009) 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.

Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry

Proportional–Integral–Derivative controller technique continues to provide the easiest and effective solutions to most of the industrial applications in recent years. However PID controller is poorly tuned in practice compared to most other tuning methods and is complicated with poor performance. This research presents a multi objective optimization approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization and Bacterial foraging optimization. The proposed multi objective optimization algorithm is used to tune the PID controller parameters and their performances have been compared with the conventional methodologies like Ziegler Nichols method. The results proved that the use of multi objective optimization approach based controller tuning improves the performance of process in terms of time domain specifications and performance index, set point tracking and regulatory changes and also provides stability. This paper describes the various multi objective optimization algorithms and its implementation to tune the PID Controller used in paper machine DCS as real time processing of a Pulp and paper industry processes.

Multi-objective optimization of a leg mechanism using genetic algorithms

Many engineering optimal design problems involve multiple conflicting objectives and often they are attempted to be solved by converting them into a single composite objective. Moreover, to be able to use standard classical optimization methods, often such problems are divided into suitable subproblems and solved in stages. A leg mechanism design problem which received some attention in the past involves link length and spring characteristics as decision variables and as many as three objectives and 17 inequality constraints. The problem is difficult to optimize because of strict geometric constraints which make only a tiny fraction of the search space feasible. This article applies an evolutionary multi-objective optimization (EMO) methodology to solve the complete leg mechanism optimization problem for all three objectives simultaneously. The way of solving the complete problem demonstrates how such a complex engineering design problem can be solved by evolutionary algorithms and shows that useful insights into the design problem can be obtained by systematically starting with fewer objectives and gradually adding more objectives. Several optimization concepts are introduced to gain confidence in the obtained solutions. Results of this study are compared with that of an earlier study and in all cases the superiority and flexibility of the EMO approach is demonstrated. The ease and efficiency of the EMO methodology demonstrated in this article should encourage similar studies involving other mechanical component design problems.