Solving the optimal control problem with a free final time, such as suborbital launch vehicle (SLV) trajectory optimization with two control variables and multi-constraints ones based on particle swarm optimization (P...
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The analysis of the carotid artery wall is of paramount importance in clinical practice. Especially, the intima-media thickness is a risk index for some of the most severe acute cerebrovascular pathologies, hence, an ...
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In this paper, a nonlinear chaotic system is presented, which is derived from the chua's circuit with a memristor. This is a four-dimensional autonomous circuit which exhibits chaotic behavior. The chaotic system ...
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Many real world problems involve the simultaneous optimization of various and often conflicting objectives. These optimization problems are known as multi-objective optimization problems. Evolutionary multi-objective ...
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Many real world problems involve the simultaneous optimization of various and often conflicting objectives. These optimization problems are known as multi-objective optimization problems. Evolutionary multi-objective optimization, whose main task is to deal with multi-objective optimization problems by evolutionary computation techniques, has become a hot topic in evolutionary computation community. The solution diversity of multi-objective optimization problems mainly focuses on two aspects, breadth and uniformity. After analyzing the traditional methods which were used to maintain the diversity of individual in multi-objective evolutionary algorithms, a novel nondominated individual selection strategy based on adaptive partition is proposed. The new strategy partitions the current trade-off front adaptively according to the individual's similarity. Then one representative individual will be selected in each partitioned regions for pruning nondominated individuals. For maintaining the diversity of the solutions, the adaptive partition selection strategy can be incorporated in multi-objective evolutionary algorithms without the need of any parameter setting, and can be applied in either the parameter or objective domain depending on the nature of the problem involved. In order to evaluate the validity of the new strategy, we apply it into two state-of-the-art multi-objective evolutionary algorithms. The experimental results based on thirteen benchmark problems show that the new strategy improves the performance obviously in terms of breadth and uniformity of nondominated solutions.
An efficient feature extraction method based on the Curvelet Transform for detecting human in static images is proposed in this paper. The edge features can be extracted with the block-based statistical information of...
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Common algorithmic problem is an optimization problem, which has the nice property that several other NPcomplete problems can be reduced to it in linear time. A tissue P system with cell division is a computing model ...
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An Echo State Network (ESN) based predictive control method for aircraft system is proposed to increase the flight quality. Aerocraft is a nonlinear and time-varying system and its controllers are usually designed by ...
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In this paper, we formulate and investigate a memristor-based switching network which is directly extended from Itoh and Chua's chaotic circuit. Conditions are derived which ensure the existence of an equilibrium ...
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The analytical algorithm of program quaternion is studied, aiming at the problem of the arbitrary spacecraft attitude-adjusting control. It also provides the analytical constructor method of the program quaternion for...
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Reentry trajectory optimization is a multi-constraints optimal control problem which is hard to solve. To tackle it, we proposed a new algorithm named CDEN(Constrained Differential Evolution Newton-Raphson Algorithm) ...
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Reentry trajectory optimization is a multi-constraints optimal control problem which is hard to solve. To tackle it, we proposed a new algorithm named CDEN(Constrained Differential Evolution Newton-Raphson Algorithm) based on Differential Evolution(DE) and *** transform the infinite dimensional optimal control problem to parameter optimization which is finite dimensional by discretize control parameter. In order to simplify the problem, we figure out the control parameter's scope by process constraints. To handle constraints, we proposed a parameterless constraints handle process. Through comprehensive analyze the problem, we use a new algorithm integrated by DE and Newton-Raphson to solve it. It is validated by a reentry vehicle X-33, simulation results indicated that the algorithm is effective and robust.
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