Bacterial Foraging Algorithm Based on PSO with Adaptive Inertia Weigh for Solving Nonlinear Equations Systems

2013 ◽  
Vol 655-657 ◽  
pp. 940-947 ◽  
Author(s):  
Xiong Fa Mai ◽  
Ling Li
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Equations ◽  
Particle Swarm Optimization Algorithm ◽  
The Other ◽  
Systems Of Nonlinear Equations ◽  
Swarm Optimization ◽  
Slow Convergence ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Algorithm (BFA) has recently emerged as a very powerful technique for optimization,but it also confronts the problems of slow convergence and premature convergence. To overcome the drawbacks of BFA, This article merge the idea of particle swarm optimization algorithm with adaptive inertia weigh into the bacterial foraging to improve the speed and convergence capabilities of BFA, and according to this a bacterial foraging algorithm based on PSO(APSO-BFA) is presented. Simulation results on five systems of nonlinear equations show that the proposed algorithm is superior to the other two kinds of bacterial foraging algorithm

Quantum-Behaved Particle Swarm Optimization Algorithm for Solving Nonlinear Equations

2013 ◽  
Vol 756-759 ◽  
pp. 2926-2931 ◽  
Author(s):  
Xiao Feng Zhang ◽  
Gui Fang Sui
Keyword(s):  
Particle Swarm Optimization ◽  
Real Number ◽  
Nonlinear Equations ◽  
Optimization Algorithm ◽  
Traditional Method ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Simulation Results

A quantum-behaved particle swarm optimization algorithm is presented in this paper for solving nonlinear equations. The positions of particle are coded by probability amplitudes of qubits that are updated by quantum rotation gates in this method. The corresponding real number solution at specified interval can be extracted by this algorithm for solving nonlinear equations. Compared to real traditional method, the simulation results show that this algorithm is more accurate and effective.

Bacterial Foraging Algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 948-954 ◽  
Author(s):  
Ling Li ◽  
Xiong Fa Mai
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Local Minimum ◽  
Random Search ◽  
Particle Swarm ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the E.coli algorithm depends on random search directions which may lead to delay in reaching the global solution.The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented.The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

scholarly journals Achieving large and distant ancestral genome inference by using an improved discrete quantum-behaved particle swarm optimization algorithm

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Ruofan Xia ◽  
Gaofeng Pan ◽  
Jiandong Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Experimental Results ◽  
The Other ◽  
Ancestral Genome ◽  
Swarm Optimization ◽  
Median Approach

Abstract Background Reconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data. Results In response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median. Conclusions Our experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.

Particle Swarm Optimization Based Algorithm for Ship Vertical Passage Layout Optimization

2012 ◽  
Vol 479-481 ◽  
pp. 1885-1888
Author(s):  
Wen Quan Wang ◽  
Sheng Huang ◽  
Yu Long Hu ◽  
Yuan Hang Hou
Keyword(s):  
Particle Swarm Optimization ◽  
Rational Design ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Vital Role ◽  
Layout Problem ◽  
Swarm Optimization ◽  
Operation And Maintenance ◽  
Simulation Results ◽  
The Mathematical Model

The passage plays a vital role in operation and maintenance of naval ships. The rational design of the passage could increase the efficiency of combat logistics, life-cycle logistics and personnel movement under combat and emergency conditions. Borrowing ideas from facility layout, the model of optimal vertical passage layout was established to formulation the vertical passage layout problem. The Floyd’s algorithm was used to calculate the distance between two compartments and the particle swarm optimization algorithm was proposed to solve the mathematical model. From simulation results, the model and methods are feasible for the vertical passage layout problem.

A New Combined Particle Swarm Optimization Algorithm Based Golden Section Strategy

2011 ◽  
Vol 308-310 ◽  
pp. 1099-1105 ◽  
Author(s):  
Hui Fan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Evolution Model ◽  
The Other ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Global Evolution ◽  
Global Optimal

Based the defects of global optimal model falling into local optimum easily and local model with slow convergence speed during traditional PSO algorithm solving a complex high-dimensional and multi-peak function, a two sub-swarms particle optimization algorithm is proposed. All particles are divided into two equivalent parts. One part particles adopts global evolution model, while the other part uses local evolution model. If the global optimal fitness of the whole population stagnates for some iteration, a golden rule is introduced into local evolution model. This strategy can substitute the partial perfect particles of local evolution for the equivalent worse particles of global evolution model. So, some particles with advantage are joined into the whole population to make the algorithm keep active all the time. Compared with classic PSO and PSO-GL(A dynamic global and local combined particle swarm optimization algorithm, PSO-GL), the results show that the proposed PSO in this paper can get more effective performance over the other two algorithm in the simulation experiment for four benchmark testing function.

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