This paper presents an indicator-based multi-objective local search (IBMOLS) to solve a multi-objective optimization problem. The problem concerns the selection and scheduling of observations for an agile Earth observing satellite. The mission of an Earth observing satellite is to obtain photographs of the Earth surface to satisfy user requirements. Requests from several users have to be managed before transmitting an order, which is a sequence of selected acquisitions, to the satellite. The obtained sequence has to optimize two objectives under operation constraints. The objectives are to maximize the total profit of the selected acquisitions and simultaneously to ensure the fairness of resource sharing by minimizing the maximum profit difference between users. Experiments are conducted on realistic instances. Hypervolumes of the approximate Pareto fronts are computed and the results from IBMOLS are compared with the results from the biased random-key genetic algorithm (BRKGA).

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The optimal trajectory planning is an important function in a robot control area. Generally, the operating function of manipulators requires the highest performance such as minimum time, minimum energy, and no damage to the system. This paper proposes a minimum time trajectory planning that is clamped with cubic splines and uses Harmony Search (HS) algorithm for solving the optimization problem. Minimum time is chosen to be the objective function as time is critical for productivities in the industrial. However, kinematics constraints such as velocities, accelerations and jerks limitation are still considered. In this work, the simulation of the 6-DOFs robot manipulator trajectory is employed to determine the minimum time trajectory planning. The best solution from two techniques, the HS and the Sequential Quadratic Programming (SQP), are compared. The results show that the HS method obtains the optimal interval time better than the SQP method and it does not require finding the initial interval time value for the optimization process. This reduces the complication and time consuming of the optimization process.

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This paper presents a biased random key genetic algorithm, or BRKGA, for solving a multi-user observation scheduling problem. BRKGA is an efficient method in the area of combinatorial optimization. It is usually applied to single objective problem. It needs to be adapted for multi-objective optimization. This paper considers two adaptations. The first one presents how to select the elite set, i.e., good solutions in the population. We borrow the elite selection methods from efficient multi-objective evolutionary algorithms. For the second adaptation, since the multi-objective optimization needs a set of solutions on the Pareto front, we investigate the idea to obtain several solutions from a single chromosome. Experiments are conducted on realistic instances, which concern the multi-user observation scheduling of an agile Earth observing satellite.

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The paper presents an agricultural monitoring system developed for Thailand. Various species of plants have been directly observed from the agricultural fields which mainly consist of economic crops of Thailand such as rice, cassava, rubber, sugar cane, corn, etc. An equipment used to obtain the data is called field server, which has been installed at the observed field for a long period. The collected data is separated to two parts: daily images (acquired twice per day) and weather information (recorded every five minutes). The weather information is as follows: temperature, rain volume, light density, humidity, soil moisture, wind speed and direction. Since the beginning of 2014, twenty-four field servers have been deployed in every region of Thailand. The data from the field servers is uploaded to a central server. Users can access and obtain the data via a web browser. Given the images and weather information (temperature and soil moisture), the data recorded from paddy fields is preliminarily analyzed as a guideline for further development.