Method of the Mission Planning for the Communication between the Small Satellite Clusters

Mission planning of space astronomical satellite is a complex optimization problem, which is to determine the communication activities needed by space astronomical and research the in-orbit plan. By abstracting the relevant elements of the mission planning problems of space astronomical satellite and establishing the mathematical model of mission planning of the space astronomical satellite, we introduce the Genetic Algorithm and design the single-objective Genetic Algorithm based on the communication mission window. In addition, based on the Genetic Algorithm, a multi-objective Genetic Algorithm based on the sequence of communication window is designed, which improves the coding ability of Genetic Algorithm and improves the flexibility and applicability of planning effect. From the results of planning simulation, this paper not only innovatively introduces Genetic Algorithm into mission planning of satellite and ground data in order to improve the efficiency of mission planning of space astronomical satellite, but also optimizes single-objective mission to multi-objective mission, which improves the applicability of mission planning of satellite communication and provides reference for other relevant researches in the future.

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How Many Robots are Enough: A Multi-Objective Genetic Algorithm for the Single-Objective Time-Limited Complete Coverage Problem

2018 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra.2018.8461028 ◽

2018 ◽

Cited By ~ 3

Author(s):

Xing Zhou ◽

Huaimin Wang ◽

Bo Ding

Keyword(s):

Genetic Algorithm ◽

Coverage Problem ◽

Complete Coverage ◽

Multi Objective ◽

Objective Time ◽

Multi Objective Genetic Algorithm ◽

Single Objective

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Genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

Robotica ◽

10.1017/s0263574718000152 ◽

2018 ◽

Vol 36 (6) ◽

pp. 839-864 ◽

Cited By ~ 5

Author(s):

Abdur Rosyid ◽

Bashar El-Khasawneh ◽

Anas Alazzam

Keyword(s):

Genetic Algorithm ◽

Hybrid Algorithm ◽

Weighted Sum ◽

Parallel Kinematics ◽

Multi Objective Optimization ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Cartesian Stiffness Matrix ◽

Optimal Value ◽

Single Objective

SUMMARYThis paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

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Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

Evolutionary Computation ◽

10.1162/evco.1999.7.3.205 ◽

1999 ◽

Vol 7 (3) ◽

pp. 205-230 ◽

Cited By ~ 752

Author(s):

Kalyanmoy Deb

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Optimization Problems ◽

Test Problems ◽

Pareto Optimal ◽

Multi Objective Optimization ◽

Pareto Optimal Front ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Single Objective

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

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