Improved Chaotic Bacteria Foraging Optimization Algorithm Particle

2014 ◽  
Vol 651-653 ◽  
pp. 2322-2325
Author(s):  
Ying Ai ◽  
Yi Xin Su ◽  
Dan Hong Zhang ◽  
Yao Peng
Keyword(s):  
Optimization Algorithm ◽  
Chaotic Map ◽  
Standard Test ◽  
Convergence Speed ◽  
Test Functions ◽  
Algorithm Optimization ◽  
Slow Convergence ◽  
And Migration ◽  
Logistic Chaotic Map ◽  
Bacteria Foraging Optimization Algorithm

. Aiming at the defects of weak global search ability and slow convergence speed in bacteria foraging algorithm optimization, this paper proposed an improved chaotic bacteria foraging optimization algorithm which has introduced the chaotic thoughts, improved the update operation of fitness and migration operation in optimization process. Using Logistic chaotic map initializes the bacteria population, so as to improve the convergence speed of the algorithm; Then adjust quorum sensing mechanism to optimize the chemotactic direction of the bacteria, and operate on perished bacteria with chaos disturbance in migration operation, so as to improve the global optimization ability of the algorithm. Simulation of two standard test functions show that the proposed algorithm has higher convergence speed and precision.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

An Improved DE Algorithm Based on the New Crossover Strategy

2015 ◽  
Vol 713-715 ◽  
pp. 1583-1588
Author(s):  
Cao Liang Liang ◽  
Wang Rui Rong ◽  
Liu Man Dan
Keyword(s):  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Standard Test ◽  
Local Solution ◽  
Population Diversity ◽  
Test Functions ◽  
Algorithm Optimization ◽  
De Algorithm ◽  
Local Optimal Solution ◽  
Evolution Algorithm

Differential Evolution Algorithm (DE) is fast and stable, but it’s easy to fall into the local optimal solution and the population diversity reduces fast in the later period. In order to improve the algorithm optimization and convergence capability, this paper proposes an improved DE algorithm based on the new crossover strategy (CMDE). As to the Crossover-factor is decided by the proportion of the variance and the evolution process in each generation, so it can follow the process of evolution and constantly change; the added operation of Second Mutation can improve the capacity of solving problem, which algorithm falls into the local solution easily. With four standard test functions, the results show that the CMDE algorithm is superior to DE in convergence speed, precise and stability of algorithm.

scholarly journals Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

2021 ◽  
Vol 11 (16) ◽  
pp. 7358
Author(s):  
Linlin Li ◽  
Shufang Xu ◽  
Hua Nie ◽  
Yingchi Mao ◽  
Shun Yu
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Chaotic Map ◽  
Poor Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Local Optimum ◽  
Search Problem ◽  
Target Search ◽  
Disturbance Factor

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.

scholarly journals A Novel Particle Swarm Optimization Algorithm for Global Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Chun-Feng Wang ◽  
Kui Liu
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Optimization Method ◽  
Standard Test ◽  
Test Functions ◽  
Swarm Optimization ◽  
Chaotic Search

Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.

A double flora bacteria foraging optimization algorithm

2014 ◽  
Vol 31 (1) ◽  
pp. 43 ◽  
Author(s):  
Jianguo Jiang ◽  
Jiawei Zhou ◽  
Yingchun Zheng ◽  
Runsheng Zhou
Keyword(s):  
Optimization Algorithm ◽  
Bacteria Foraging Optimization Algorithm

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

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