Research on Continuous Function Optimization Algorithm Based on Swarm-Intelligence

2009 ◽  
Author(s):  
Ma Sasa ◽  
Liu Dongqing ◽  
Xue Jia ◽  
Fang Xingqiao
Keyword(s):  
Continuous Function ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Function Optimization

Convergence Analysis of Hybrid Free Search and Invasive Weed Optimization Algorithm

2011 ◽  
Vol 143-144 ◽  
pp. 329-334
Author(s):  
Lu Li ◽  
Xing Yu Wang ◽  
Zi Hou Zhang ◽  
Liu Bin Fan
Keyword(s):  
Continuous Function ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Function Optimization ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Late Evolution ◽  
Hybrid Intelligence ◽  
Free Search

Considering the fitness of each individual, a hybrid intelligence algorithm is established, which combine the excellent probing ability of free search algorithm (FS) with exploiting ability of invasive weed optimization algorithm (IWO). The hybrid algorithm can overcome the disadvantage of lower optimization rate in late evolution for FS and taking advantage of powerful exploiting abilities for IWO. Identity between FS and IWO is analyzed and convergence of the two algorithms in solving continuous function optimization is provided. Simulations confirmed the analysis. Multi-model Shubert function is chosen to carry out the simulation. Compared with FS and IWO, the hybrid algorithm is superior in convergence speed and robustness.

scholarly journals A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Daqing Wu ◽  
Jianguo Zheng
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Benchmark Problems ◽  
Particle Swarm Optimizer ◽  
Hybrid Swarm ◽  
Swarm Intelligence Optimization

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

scholarly journals An enhanced chimp optimization algorithm for continuous optimization domains

2021 ◽  
Author(s):  
Heming Jia ◽  
Kangjian Sun ◽  
Wanying Zhang ◽  
Xin Leng
Keyword(s):  
Social Status ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Rank Correlation ◽  
Function Optimization ◽  
Local Optimum ◽  
Hunting Behavior ◽  
Spearman’S Rank Correlation ◽  
Engineering Design Problems ◽  
Application Fields

AbstractChimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

scholarly journals A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Minerals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 159
Author(s):  
Nan Lin ◽  
Yongliang Chen ◽  
Haiqi Liu ◽  
Hanlin Liu
Keyword(s):  
Machine Learning ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Geochemical Data ◽  
Support Vector ◽  
Learning Models ◽  
Hyperparameter Optimization ◽  
Mineral Prospectivity ◽  
Swarm Intelligence Optimization ◽  
Machine Learning Models

Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

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