XJTLU Research Repository

1. Design Similarity Measure and Application to Fault Detection of Lateral Directional Mode Flight System

Author:Park, W;Lee, S;Lee, S;Ting, TO

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT II,2012,Vol.7332

Abstract:In this work, we first obtained the similarity measures. The obtained similarity measures were designed based on well-known Hamming distance. It was also considered by analyzing the certainty and uncertainty of the fuzzy membership functions. The proposed similarity measure was applied to the fault detection of primary control surface stuck of Uninhabited Aerial Vehicle (UAV). At post-failure control surface, if the UAV has controllable and trimmable using other control surfaces, the UAV is able to fly or returns to the safety region through reconfiguration of the flight control system. By the calculation of similarity measure, result could be applicable with the real-time parameter estimation method. Furthermore, coefficients monitoring make it possible to monitor the occurrence of control surface fault. The obtained result has the advantage of increasing reliability without adding sensors or any additional cost.

2. A Novel Search Interval Forecasting Optimization Algorithm

Author:Lou, Y;Li, JL;Shi, YH;Jin, LP

Source:ADVANCES IN SWARM INTELLIGENCE, PT I,2011,Vol.6728

Abstract:In this paper, we propose a novel search interval forecasting (SIF) optimization algorithm for global numerical optimization. In the SIF algorithm, the information accumulated in the previous iteration of the evolution is utilized to forecast area where better optimization value can be located with the highest probability for the next searching operation. Five types of searching strategies are designed to accommodate different situations, which are determined by the history information. A suit of benchmark functions are used to test the SIF algorithm. The simulation results illustrate the good performance of SIF, especially for solving large scale optimization problems.

3. Brain Storm Optimization Algorithm

Author:Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, PT I,2011,Vol.6728

Abstract:Human being is the most intelligent animal in this world. Intuitively, optimization algorithm inspired by human being creative problem solving process should be superior to the optimization algorithms inspired by collective behavior of insects like ants, bee, etc. In this paper, we introduce a novel brain storm optimization algorithm, which was inspired by the human brainstorming process. Two benchmark functions were tested to validate the effectiveness and usefulness of the proposed algorithm.

4. Particle Filter Optimization: A Brief Introduction

Author:Liu, B;Cheng, S;Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT I,2016,Vol.9712

Abstract:In this paper, we provide a brief introduction to particle filter optimization (PFO). The particle filter (PF) theory has revolutionized probabilistic state filtering for dynamic systems, while the PFO algorithms, which are developed within the PF framework, have not attracted enough attention from the community of optimization. The purpose of this paper is threefold. First, it aims to provide a succinct introduction of the PF theory which forms the theoretical foundation for all PFO algorithms. Second, it reviews PFO algorithms under the umbrella of the PF theory. Lastly, it discusses promising research directions on the interface of PF methods and swarm intelligence techniques.

5. Brain Storm Optimization with Agglomerative Hierarchical Clustering Analysis

Author:Chen, JF;Wang, JY;Cheng, S;Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT II,2016,Vol.9713

Abstract:Brain storm optimization (BSO) is a relatively new swarm intelligence algorithm, which simulates the problem-solving process of human brainstorming. In General, BSO employs flat clustering which has a number of drawbacks. In this paper, the agglomerative hierarchical clustering is introduced into BSO and its impact on the performance of the creating operator is then analyzed. The proposed algorithm is applied to numerical optimization problems in comparison with the BSO with kappa-means Clustering. Experimental results show that the proposed algorithm achieves satisfactory results and guarantees a high coverage rate.

6. Brain Storm Optimization Algorithm for Multi-objective Optimization Problems

Author:Xue, JQ;Wu, YL;Shi, YH;Cheng, S

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I,2012,Vol.7331

Abstract:In this paper, a novel multi-objective optimization algorithm based on the brainstorming process is proposed(MOBSO). In addition to the operations used in the traditional multi-objective optimization algorithm, a clustering strategy is adopted in the objective space. Two typical mutation operators, Gaussian mutation and Cauchy mutation, are utilized in the generation process independently and their performances are compared. A group of multi-objective problems with different characteristics were tested to validate the effectiveness of the proposed algorithm. Experimental results show that MOBSO is a very promising algorithm for solving multi-objective optimization problems.

7. Parameter Estimation of Vertical Two-Layer Soil Model via Brain Storm Optimization Algorithm

Author:Ting, TO;Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT I,2016,Vol.9712

Abstract:A practical soil model is derived mathematically based on the measurement principles of Wenner's method. The Wenner's method is a conventional approach to measuring the apparent soil resistivity. This soil model constitutes two-soil layer with different properties vertically. Thus this model is called the vertical two-layer soil model. The motivation for the mathematical model is to estimate relevant parameters accurately from the data obtained from site measurements. This parameter estimation is in fact a challenging optimization problem. From the plotted graphs, this problem features a continuous but non-smooth landscape with a steep alley. This poses a great challenge to any optimization tool. Two prominent algorithms are applied, namely Gauss-Newton (GN) and Brain Storm Optimization (BSO). Results obtained conclude that the GN is fast but diverges due to bad starting points. On the contrary, the BSO is slow but it never diverges and is more stable.

8. The Evaluation of Data Uncertainty and Entropy Analysis for Multiple Events

Author:Lee, S;Ting, TO

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT II,2012,Vol.7332

Abstract:In this paper, data analysis for multiple facts was carried out via fuzzy entropy. The entropy for the fuzzy data with respect to multiple facts was designed through distance measure. The obtained fuzzy entropy was used to analyze the uncertainties with respect to each fact. By summarizing fuzzy entropy, data uncertainty information was limited by the total fact (n) minus one, that is, n-1. The bounded calculation of data uncertainty to each fact was also proven for multiple facts and the decision of fuzzy data to the certain fact among multiple facts has been considered with the help of fuzzy entropy calculation.

9. Normalized Population Diversity in Particle Swarm Optimization

Author:Cheng, S;Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, PT I,2011,Vol.6728

Abstract:Particle swarm optimization (PSO) algorithm can be viewed as a series of iterative matrix computation and its population diversity can be considered as an observation of the distribution of matrix elements. In this paper, PSO algorithm is first represented in the matrix format, then the PSO normalized population diversities are defined and discussed based on matrix analysis. Based on the analysis of the relationship between pairs of vectors in PSO solution matrix, different population diversities are defined for separable and non-separable problems, respectively. Experiments on benchmark functions are conducted and simulation results illustrate the effectiveness and usefulness of the proposed normalized population diversities.

10. Inertia Weight Adaption in Particle Swarm Optimization Algorithm

Author:Zhou, Z;Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, PT I,2011,Vol.6728

Abstract:In Particle Swarm Optimization (PSO), setting the inertia weight w is one of the most important topics. The inertia weight was introduced into PSO to balance between its global and local search abilities. In this paper, first, we propose a method to adaptively adjust the inertia weight based on particle's velocity information. Second, we utilize both position and velocity information to adaptively adjust the inertia weight. The proposed methods are then tested on benchmark functions. The simulation results illustrate the effectiveness and efficiency of the proposed algorithm by comparing it with other existing PSOs.

11. Exponential Inertia Weight for Particle Swarm Optimization

Author:Ting, TO;Shi, YH;Cheng, S;Lee, S

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I,2012,Vol.7331

Abstract:The exponential inertia weight is proposed in this work aiming to improve the search quality of Particle Swarm Optimization (PSO) algorithm. This idea is based on the adaptive crossover rate used in Differential Evolution (DE) algorithm. The same formula is adopted and applied to inertia weight, w. We further investigate the characteristics of the adaptive w graphically and careful analysis showed that there exists two important parameters in the equation for adaptive w; one acting as the local attractor and the other as the global attractor. The 23 benchmark problems are adopted as test bed in this study; consisting of both high and low dimensional problems. Simulation results showed that the proposed method achieved significant improvement compared to the linearly decreasing method technique that is used widely in literature.

12. Brain Storm Optimization in Objective Space Algorithm for Multimodal Optimization Problems

Author:Cheng, S;Qin, QD;Chen, JF;Wang, GG;Shi, YH

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2016, PT I,2016,Vol.9712

Abstract:The aim of multimodal optimization is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. In this paper, brain storm optimization in objective space (BSO-OS) algorithm is utilized to solve multimodal optimization problems. Our goal is to measure the performance and effectiveness of BSO-OS algorithm. The experimental tests are conducted on eight benchmark functions. Based on the experimental results, the conclusions could be made that the BSO-OS algorithm performs good on solving multimodal optimization problems. To obtain good performances on multimodal optimization problems, an algorithm needs to balance its global search ability and solutions maintenance ability.

13. On the Performance Metrics of Multiobjective Optimization

Author:Cheng, S;Shi, YH;Qin, QD

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I,2012,Vol.7331

Abstract:Multiobjective Optimization (MOO) refers to optimization problems that involve two or more objectives. Unlike in the single objective optimization, a set of solutions representing the tradeoff among the different objects rather than an unique optimal solution is sought in MOO. How to measure the goodness of solutions and the performance of algorithms is important in MOO. In this paper, we first review the performance metrics of multiobjective optimization and then classify variants of performance metrics into three categories: set based metrics, reference point based metrics, and the true Pareto front/set based metrics. The properties and drawbacks of different metrics are discussed and analyzed. From the analysis of different metrics, an algorithm's properties can be revealed and more effective algorithms can be designed to solve MOO problems.

14. Brain Storm Optimization Algorithm with Modified Step-Size and Individual Generation

Author:Zhou, DD;Shi, YH;Cheng, S

Source:ADVANCES IN SWARM INTELLIGENCE, ICSI 2012, PT I,2012,Vol.7331

Abstract:Brain Storm Optimization algorithm is inspired from the humans' brainstorming process. It simulates the problem-solving process of a group of people. In this paper, the original BSO algorithm is modified by amending the original BSO. First the step-size is adapted according to the dynamic range of individuals on each dimension. Second, the new individuals are generated in a batch-mode and then selected into the next generation. Experiments are conducted to demonstrate the performance of the modified BSO by testing on ten benchmark functions. The experimental results show that the modified BSO algorithm performs better than the original BSO.

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