scholarly journals Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Lei Xie ◽  
Tong Han ◽  
Huan Zhou ◽  
Zhuo-Ran Zhang ◽  
Bo Han ◽  
...  
Keyword(s):  
Global Optimization ◽  
Foraging Behavior ◽  
Metaheuristic Algorithm ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Wilcoxon Rank Sum Test ◽  
Engineering Problems ◽  
Cooperative Foraging ◽  
Simulation Results

In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms.

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.

The optimal synchronizability of complex networks with saturated coupling strength

2020 ◽  
Vol 31 (10) ◽  
pp. 2050139
Author(s):  
Chen Huang ◽  
Xinbiao Lu ◽  
Jun Zhou ◽  
Buzhi Qin
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Network Topology ◽  
Coupling Strength ◽  
Small World ◽  
Pso Algorithm ◽  
Small World Network ◽  
Swarm Optimization ◽  
Edge Betweenness ◽  
Simulation Results

For networks with fixed network topology, when the total coupling strength between nodes is limited and the coupling strength between nodes is saturated, the global optimization algorithms including genetic algorithm (GA) and particle swarm optimization (PSO) algorithm are used to adjust the coupling strength between nodes to improve the synchronizability of the network, respectively. Simulation results show that in WS small-world network, when the edge betweenness centrality of the edge is large, the coupling strength of the edge after optimization is greater. Furthermore, compared with GA, PSO has better performance.

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

Study on Vehicle Logistics Routing Path Problem with Time Windows Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao
Keyword(s):  
Particle Swarm Optimization ◽  
World Economy ◽  
Time Window ◽  
Time Windows ◽  
Particle Swarm ◽  
Modern Logistics ◽  
Swarm Optimization ◽  
Logistics Industry ◽  
Search Speed ◽  
Simulation Results

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.

Precision Motion of Iterative Learning Controller Using Adaptive Filter Bandwidth Tuning by Improved Particle Swarm Optimization Technique

2013 ◽  
Vol 376 ◽  
pp. 349-353
Author(s):  
Yi Cheng Huang ◽  
Shu Ting Li ◽  
Kuan Heng Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Iterative Learning ◽  
Swarm Optimization ◽  
Filter Bandwidth ◽  
Improved Particle Swarm Optimization ◽  
Simulation Results ◽  
Adaptive Bandwidth ◽  
Precision Motion ◽  
Zero Phase

This paper utilized the Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of PID and the bandwidth of zero-phase Butterworth Filter of an Iterative Learning Controller (ILC) for precision motion. Simulation results show that IPSO-ILC-PID controller without adaptive bandwidth filter tuning have the chance of producing high frequencies in the error signals when the filter bandwidth is fixed for every repetition. However the learnable and unlearnable error signals should be separated for bettering control process. Thus the adaptive bandwidth of a zero phase filter in ILC-PID controller with IPSO tuning is applied to one single motion axis of a CNC table machine. Simulation results show that the developed controller can cancel the errors efficiently as repetition goes. The frequency response of the error signals is analyzed by the empirical mode decomposition (EMD) and the Hilbert-Huang Transform (HHT) method. Errors are reduced and validated by ILC with adaptive bandwidth filtering design.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

2013 ◽  
Vol 46 (11) ◽  
pp. 1465-1484 ◽  
Author(s):  
Weian Guo ◽  
Wuzhao Li ◽  
Qun Zhang ◽  
Lei Wang ◽  
Qidi Wu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Engineering Problems

Reliability-Based Analysis and Optimization of the Gravity Balancing Performance of Spring-Articulated Serial Robots with Uncertainties

2021 ◽  
pp. 1-32
Author(s):  
Vu Linh Nguyen ◽  
Chin-Hsing Kuo ◽  
Po Ting Lin
Keyword(s):  
Pso Algorithm ◽  
Reliability Based Design Optimization ◽  
Swarm Optimization ◽  
Reliability Based Design ◽  
Serial Robots ◽  
Simulation Based ◽  
Serial Robot ◽  
Simulation Results ◽  
Uncertainty Level

Abstract This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. The reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. The gravity balancing begins with a simulation-based analysis of the gravitational torques of a typical serial robot. Based on the simulation results, a gravity balancing design for the robot using mechanical springs is realized. A reliability-based design optimization (RBDO) method is also developed to seek a reliable and robust design for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.

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