Self-adaptive bacterial foraging algorithm based on estimation of distribution

2020 ◽  
pp. 1-13
Author(s):  
Na Ni ◽  
Yuanguo Zhu
Keyword(s):  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Step Size ◽  
Bacterial Foraging ◽  
Fitness Value ◽  
New Locations ◽  
Estimation Of Distribution ◽  
Local Optimal Solution ◽  
Bacterial Foraging Algorithm ◽  
Self Adaptive

Bacteria foraging optimization (BFO) algorithm is easy to fall into the local optimal solution and slow in convergence. In this paper, we have come up with a self-adaptive bacterial foraging algorithm based on estimation of distribution to overcome the mentioned shortages. First, in the chemotactic operator, the swimming step size of bacterium is adaptively adjusted by its fitness value and bacteria move in a random direction. Second, the bacteria obtain the probability of replication based on the fitness value. We choose half of the population for replication by the roulette wheel method. Finally, the possibility of elimination-dispersal is adjusted by the fitness value. Selected bacteria are dispersed to the new locations produced by BOX-Muller formula. Compared with some relative heuristic algorithms on finding the optimal value of ten benchmark functions, the proposed algorithm shows higher convergence speed and accuracy.

scholarly journals Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Li ◽  
Hua Zhu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Ant Colony ◽  
Multidimensional Space ◽  
Bacterial Foraging ◽  
Operation Speed ◽  
Local Optimal Solution ◽  
Bacterial Foraging Algorithm ◽  
Selection For

The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

Image Matching Based on Bacterial Foraging Algorithm

2011 ◽  
Vol 301-303 ◽  
pp. 859-863
Author(s):  
Hong Peng Tian
Keyword(s):  
Wavelet Transform ◽  
Swarm Intelligence ◽  
Image Matching ◽  
Fitness Function ◽  
Optimal Solution ◽  
Experimental Results ◽  
Matching Method ◽  
Bacterial Foraging ◽  
Template Image ◽  
Bacterial Foraging Algorithm

To increase the speed of image matching, this paper combines Bacterial Foraging Algorithm (BFA) of swarm intelligence with wavelet transform, and presents a fast matching method. The method regards the problem of image matching as a search for the optimal solution. To provide artificial bacterial swarm algorithm with an appropriate fitness function, the Normalized Product correlation (NPROD) is employed to measure the similarity between the template image and the searching image. Then the best coarse matching position is gradually approaching by chemotaxis, elimination and dispersal, and reproduction behaviors of artificial bacterial. Finally, the best matching position is found out according to the coarse matching position. Experimental results show that the proposed method is fast and efficient.

PSO Algorithm Based Task Allocation in Dynamic Virtual Enterprise

2011 ◽  
Vol 189-193 ◽  
pp. 2572-2576
Author(s):  
Jian Hua Jiang ◽  
Bu Yun Sheng ◽  
Li Xiong Gong ◽  
Ming Zhong Yang
Keyword(s):  
Task Allocation ◽  
Virtual Enterprise ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Order Task ◽  
Fitness Value ◽  
Local Optimal Solution ◽  
Decision Making Model ◽  
Computing Method

To solve the order task allocation in Dynamic Virtual Enterprise (DVE), a multi-goal decision-making model was constructed. As to the model, an improved Particle Swarm Optimization (PSO) algorithm based order task allocation method was presented on the basis of analyzing the traditional PSO algorithm. The method overcome the deficiencies of prematurely and trapped into local optimal solution easily in traditional PSO by adjusting the parameters in algorithm automatically and introducing mutation operation from genetic algorithm. And the TOPSIS based computing method of particle fitness value was researched. Finally, the feasibility of the method was verified by an application example.

scholarly journals Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Huang Chen ◽  
Lide Wang ◽  
Jun Di ◽  
Shen Ping
Keyword(s):  
Information Exchange ◽  
Global Optimum ◽  
The Self ◽  
Local Optimum ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Wide Range ◽  
Swarm Intelligence Optimization ◽  
Self Adaptive

Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

A Self-Adaptive and Variable Step Length Alopex Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 4302-4307
Author(s):  
Dong Li
Keyword(s):  
Optimal Solution ◽  
Step Length ◽  
Global Optimal Solution ◽  
Optimum Algorithm ◽  
Variable Step ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Simulation Results ◽  
Self Adaptive

Alopex is a heuristic and optimum algorithm. A self-adaptive and variable step length Alopex algorithm was raised to exceed local optimal solution and to approximate global optimal solution based on modified Alopex algorithm. To improve modified Alopex approximation precision further and eliminate the follow-on shocks appearance, the reasonable alter of δin was implemented. The simulation results show algorithm optimized is practicable and effective.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

Well Placement Technique and Production Optimization for Mature Field - A Case Study of L-X Field

2020 ◽  
Vol 46 ◽  
pp. 168-179
Author(s):  
Patrick Nwafor ◽  
Kelani Bello
Keyword(s):  
Oil And Gas ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Oil And Gas Industry ◽  
Optimization Method ◽  
Simulation Software ◽  
Production Optimization ◽  
Direct Optimization ◽  
Well Placement ◽  
Local Optimal Solution

A Well placement is a well-known technique in the oil and gas industry for production optimization and are generally classified into local and global methods. The use of simulation software often deployed under the direct optimization technique called global method. The production optimization of L-X field which is at primary recovery stage having five producing wells was the focus of this work. The attempt was to optimize L-X field using a well placement technique.The local methods are generally very efficient and require only a few forward simulations but can get stuck in a local optimal solution. The global methods avoid this problem but require many forward simulations. With the availability of simulator software, such problem can be reduced thus using the direct optimization method. After optimization an increase in recovery factor of over 20% was achieved. The results provided an improvement when compared with other existing methods from the literatures.

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