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.