Social Learning Particle Swarm optimization (SLPSO) is an improved Particle Swarm optimization (PSO) algorithm, which greatly improves the optimization performance of PSO. However, SLPSO still has some deficiency, suc...
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Social Learning Particle Swarm optimization (SLPSO) is an improved Particle Swarm optimization (PSO) algorithm, which greatly improves the optimization performance of PSO. However, SLPSO still has some deficiency, such as poor balance between exploration and exploitation and low search efficiency, so that it cannot yet do well in solving many complex optimization problems. Thus, this paper proposes an improved SLPSO algorithm, that is, Differential mutation and novel Social learning PSO (DSPSO). Firstly, in order to balance exploration and exploitation better, a dynamic inertia weight is introduced to replace the random inertia weight of SLPSO, and a single-example learning approach and an example-mean learning one are proposed to replace the imitation component and the social influence component of SLPSO respectively. Secondly, the dimension-based velocity updating equation of SLPSO is divided into two particle-based updating equations with the two approaches, and the two are executed alternately to form a novel social learning PSO (NSLPSO), which enhance the exploitation of SLPSO. Finally, a dynamic differential mutation strategy is used in NSLPSO to update the three best particles to enhance the exploration to obtain DSPSO. Experimental results on the complex functions from CEC2013 reveal that DSPSO outperforms SLPSO and quite a few state-of-the-art and classic PSO variants. (C) 2018 Elsevier Inc. All rights reserved.
The path planning method using intelligence optimization algorithm cannot meet the unmanned aerial vehicle (UAV) mobility constraints because of its spatial grid division. Besides, the path smoothing method of interpo...
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ISBN:
(纸本)9781467398725
The path planning method using intelligence optimization algorithm cannot meet the unmanned aerial vehicle (UAV) mobility constraints because of its spatial grid division. Besides, the path smoothing method of interpolation after path planning increases the amount and complexity of algorithm. Considering to solve UAV path planning smoothing problem in the process of scenario modelling, this paper proposed an adaptive grid model, which provide the sparsest grid division under the UAV mobility constraints. The simulation results implicate that the path using proposed model satisfies mobility constraints and decreases the computational complexity effectively in both 2-D and 3-D path planning.
In view of slow convergence speed, large steady mean square error(MSE), and existing blind phase for the constant modulus blind equalization algorithm(CMA), a multi-modulus blind equalization algorithm based on memeti...
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ISBN:
(纸本)9781510812055
In view of slow convergence speed, large steady mean square error(MSE), and existing blind phase for the constant modulus blind equalization algorithm(CMA), a multi-modulus blind equalization algorithm based on memetic algorithm(MA-MMA) is proposed, which combines the basic idea of intelligent optimizationalgorithm and introduces the individual own evolution and social behavior among individuals to the blind equalization technology. In this proposed algorithm, the reciprocal of the cost function of multi-modulus blind equalization algorithm(MMA) is defined as the fitness function of the memetic algorithm(MA), the initial optimal weight vector of the MMA is optimized by using the global information sharing mechanism and local depth search ability of the MA. When the initial optimum weight vector of the MMA is obtained, the weight vector of the MMA may be updated. The simulation results with the higher-order APSK multi-modulus signals show that, compared with the CMA, the MMA, and the multi-modulus blind equalization algorithm based on genetic algorithm(GA-MMA), the proposed MA-MMA has the fastest convergence speed, smallest mean square error(MSE), and clearest output constellations.
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