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.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

scholarly journals Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Betania Hernández-Ocaña ◽  
Ma. Del Pilar Pozos-Parra ◽  
Efrén Mezura-Montes ◽  
Edgar Alfredo Portilla-Flores ◽  
Eduardo Vega-Alvarado ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Mechanical Design ◽  
Differential Evolution Algorithm ◽  
Fine Tuning ◽  
Local Optimum ◽  
Bacterial Foraging Optimization ◽  
Planar Mechanisms ◽  
Bacterial Foraging ◽  
Evolution Algorithm ◽  
Parameter Values

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1956007
Author(s):  
Zhigao Zeng ◽  
Lianghua Guan ◽  
Wenqiu Zhu ◽  
Jing Dong ◽  
Jun Li
Keyword(s):  
Face Recognition ◽  
Kernel Function ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Function Selection ◽  
Dispersal Probability ◽  
Bacterial Foraging Optimization Algorithm

Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.

A new dynamic bacterial foraging optimization and its application on model reduction

2015 ◽  
Vol 06 (02) ◽  
pp. 1550018
Author(s):  
Shenli Wu ◽  
Sun'an Wang ◽  
Xiaohu Li
Keyword(s):  
Model Reduction ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Poor Performance ◽  
Step Size ◽  
Bacterial Foraging Optimization ◽  
Bacterial Populations ◽  
E Coli ◽  
Bacterial Foraging ◽  
Complex Optimization

Inspired by the foraging behavior of E. coli bacteria, bacterial foraging optimization (BFO) has emerged as a powerful technique for solving optimization problems. However, BFO shows poor performance on complex and high-dimensional optimization problems. In order to improve the performance of BFO, a new dynamic bacterial foraging optimization based on clonal selection (DBFO-CS) is proposed. Instead of fixed step size in the chemotaxis operator, a new piecewise strategy adjusts the step size dynamically by regulatory factor in order to balance between exploration and exploitation during optimization process, which can improve convergence speed. Furthermore, reproduction operator based on clonal selection can add excellent genes to bacterial populations in order to improve bacterial natural selection and help good individuals to be protected, which can enhance convergence precision. Then, a set of benchmark functions have been used to test the proposed algorithm. The results show that DBFO-CS offers significant improvements than BFO on convergence, accuracy and robustness. A complex optimization problem of model reduction on stable and unstable linear systems based on DBFO-CS is presented. Results show that the proposed algorithm can efficiently approximate the systems.

scholarly journals Enhanced Bacterial Foraging Optimization Based on Progressive Exploitation Toward Local Optimum and Adaptive Raid

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 95725-95738 ◽  
Author(s):  
Dongxing Wang ◽  
Xu Qian ◽  
Xiaojuan Ban ◽  
Boyuan Ma ◽  
Yan Ma ◽  
...  
Keyword(s):  
Local Optimum ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging

A reinforcement learning approach to coordinate exploration with limited communication in continuous action games

2016 ◽  
Vol 31 (1) ◽  
pp. 77-95
Author(s):  
Abdel Rodríguez ◽  
Peter Vrancx ◽  
Ricardo Grau ◽  
Ann Nowé
Keyword(s):  
Reinforcement Learning ◽  
Joint Action ◽  
Learning Automata ◽  
Global Optimum ◽  
Common Interest ◽  
Local Optimum ◽  
Continuous Action ◽  
Wide Range ◽  
Discrete Action ◽  
New Formulation

AbstractLearning automata are reinforcement learners belonging to the class of policy iterators. They have already been shown to exhibit nice convergence properties in a wide range of discrete action game settings. Recently, a new formulation for a continuous action reinforcement learning automata (CARLA) was proposed. In this paper, we study the behavior of these CARLA in continuous action games and propose a novel method for coordinated exploration of the joint-action space. Our method allows a team of independent learners, using CARLA, to find the optimal joint action in common interest settings. We first show that independent agents using CARLA will converge to a local optimum of the continuous action game. We then introduce a method for coordinated exploration which allows the team of agents to find the global optimum of the game. We validate our approach in a number of experiments.

A Self-Adaptive Step Glowworm Swarm Optimization Approach

2019 ◽  
Vol 18 (01) ◽  
pp. 1950004 ◽  
Author(s):  
Peng Qiong ◽  
Yifan Liao ◽  
Peng Hao ◽  
Xiaonia He ◽  
Chen Hui
Keyword(s):  
Step Length ◽  
Local Optimum ◽  
Optimization Approach ◽  
Step Size ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Peak Function ◽  
Solution Accuracy ◽  
Adaptive Step ◽  
Self Adaptive

When the basic glowworm swarm optimization (GSO) algorithm optimizes the multi-peak function, the solution accuracy is not high, the later convergence is slow. To solve these problems, the fluorescent factor is introduced to adaptively adjust the step length of the firefly, an adaptive step length firefly optimization algorithm is proposed, this algorithm is an improved self-adaptive step glowworm swarm optimization (ASGSO). In this algorithm, the behavior of glowworms are developed, the step size is dynamically adjusted by the fluorescent factor, the algorithm avoids falling into a local optimum and improves the optimization speed and accuracy. The simulation results show that the improved ASGSO can search for global optimization more quickly and precisely.

scholarly journals Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 229 ◽  
Author(s):  
Fu-Lan Ye ◽  
Chou-Yuan Lee ◽  
Zne-Jung Lee ◽  
Jian-Qiong Huang ◽  
Jih-Fu Tu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Imbalanced Data ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Optimization Algorithm

In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

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