Aiming at the problem of path planning for a mobile robot, an oriented clonal selection algorithm is proposed. Firstly, the static environment was expressed by a map with nodes and links. Secondly, the locations of ta...
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ISBN:
(纸本)9783037855652
Aiming at the problem of path planning for a mobile robot, an oriented clonal selection algorithm is proposed. Firstly, the static environment was expressed by a map with nodes and links. Secondly, the locations of target and obstacles were defined. Thirdly, an oriented mutation operator was used to accelerate the evolutionary progress. In this way, we can find an optimal solution with proposed oriented clonalalgorithm. Experiment results demonstrate that the algorithm is simple, effective, to solve the problem of robot path planning in a static environment.
In this work an approach of integration of clonal selection algorithm and immune memory based on self-organizing map(SOM) is presented to solve optimization problem. Immune memory lays the foundation for a rapid and m...
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ISBN:
(纸本)9781479902606
In this work an approach of integration of clonal selection algorithm and immune memory based on self-organizing map(SOM) is presented to solve optimization problem. Immune memory lays the foundation for a rapid and massive secondary response of immune system. Management of immune memory is important for improving performance and quality of optimum search using immune algorithm. The adaptive functionality of SOM is applied for emulation of the dynamic behavior of immune memory. From results obtained using proposed approach SOM-based management of immune memory can keep balance between exploration and exploitation for good solution quality and search performance. Besides SOM can improve the clonal selection algorithm in performance for multi-modal optimization search.
Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this pape...
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ISBN:
(纸本)9781467311526
Many real-world problems are dynamic optimization problems. In this case, the optima in the environment change dynamically. Therefore, traditional optimization algorithms disable to track and find optima. In this paper, a new multi-swarm cellular particle swarm optimization based on clonal selection algorithm (CPSOC) is proposed for dynamic environments. In the proposed algorithm, the search space is partitioned into cells by a cellular automaton. Clustered particles in each cell, which make a sub-swarm, are evolved by the particle swarm optimization and clonal selection algorithm. Experimental results on Moving Peaks Benchmark demonstrate the superiority of the CPSOC its popular methods.
The environment of UAV cooperative aerial combat is complex and changeable,with strong ***,high accuracy and real-time are required.A multi-combat step UAV dynamic weapon-target assignment(DWTA) game model is establis...
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ISBN:
(纸本)9781509009107
The environment of UAV cooperative aerial combat is complex and changeable,with strong ***,high accuracy and real-time are required.A multi-combat step UAV dynamic weapon-target assignment(DWTA) game model is established with the survival probability and weapons consumption factors for warring *** the solving method of bimatrix game Nash equilibrium point is applied to the model.A solving method based on clonal selection algorithm is *** algorithm considers both the diversity of population and the convergence *** results show the Nash equilibrium solution obtained by the algorithm is more accurate,which ensures the real-time and efficiency.
The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solu...
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ISBN:
(纸本)9780769536194
The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes an improved immune clonal selection algorithm, called improved clonal selection algorithm for the JSSP. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times, and the results indicate the effectiveness and flexibility of the immune memory clonal selection algorithm.
clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with ra...
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ISBN:
(纸本)9783037852644
clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with random disturbance is proposed. In the algorithm, at the same time, the size of the search space is decreased and the convergence speed of the algorithm is increased. In addition a model modifying device is used in the algorithm to cope with the uncertainty in the plant model. In addition a model is used in the algorithm cope with the uncertainty in the plant model. Simulations show that the convergence speed is satisfactory regardless of whether or not the plant model is precise nonlinear plants. The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law.
Particle Swarm Optimization, a nature-inspired evolutionary algorithm, has been successful in solving a wide range of real-value optimization problems. However, little attempts have been made to extend it to discrete ...
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ISBN:
(纸本)9780769535838
Particle Swarm Optimization, a nature-inspired evolutionary algorithm, has been successful in solving a wide range of real-value optimization problems. However, little attempts have been made to extend it to discrete problems. In this paper, a new particle swarm optimization method based on the clonal selection algorithm is proposed to avoid premature convergence and guarantee the diversity of the population. The experimental results show that the new algorithm not only has great advantage of convergence property over clonal selection algorithm and PSO, but also can avoid the premature convergence problem effectively.
clonal selection algorithm (CSA), inspired by the clonalselection theory, has gained much attention and wide applications. In most common forms, the CSAs use a binary representation of variables, and the emulated imm...
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ISBN:
(纸本)9783319462578;9783319462561
clonal selection algorithm (CSA), inspired by the clonalselection theory, has gained much attention and wide applications. In most common forms, the CSAs use a binary representation of variables, and the emulated immune operators, mutation, proliferation, selection, for example, are made to act on it. However, the binary representation often suffers from the so-called Hamming Cliff problem. In order to overcome this problem, a Gray-coded CSA is presented and used to solve optimization problems. The algorithm is applied to numerous bench-mark problems of numerical optimization problems and the computational results show effectiveness of the proposed algorithm.
FIR filter has some advantages, such as system stability, simple implement, and linear phase. It has been widely used in digital signal processing and other relative fields. clonal selection algorithm has been applied...
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ISBN:
(纸本)9780819473622
FIR filter has some advantages, such as system stability, simple implement, and linear phase. It has been widely used in digital signal processing and other relative fields. clonal selection algorithm has been applied successfully in solving problems like memory acquisition, multi-modal optimization and traveling salesman problem. This paper proposes a novel FIR filter design method. It combines clonal selection algorithm and window function method to achieve optimization. In the design process, float coding is adopted to increase the convergence precision. Some simulation experiments are carried out to verify the performance of the presented algorithm. The results show that the introduced method is able to design some FIR filter which is difficult for other methods. The filter design approach discussed in this paper is universal and easy to implement.
Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning cont...
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ISBN:
(纸本)9783037857649
Improved clonal selection algorithms and RBF neural network are used for solving nonlinear optimization problems and modeling respectively in iterative learning control, and a nonlinear optimal iterative learning control algorithm (NOILCA) is proposed. In this method, an improved clonal selection algorithm is used for solving the optimum input for the next iteration;another one is used to update the RBF neural network model of real plant. Compared with GA-ILC, NOILCA has faster convergence speed, and is able to deal with the problem of inaccurate plant model, can obtain satisfactory tracking through the few several iterations.
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