Showing papers by "Hui Li published in 2019"

Push and pull search for solving constrained multi-objective optimization problems

Abstract: This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the search process into two different stages: push and pull search stages. In the push stage, a multi-objective evolutionary algorithm (MOEA) is used to explore the search space without considering any constraints, which can help to get across infeasible regions very quickly and to approach the unconstrained Pareto front. Furthermore, the landscape of CMOPs with constraints can be probed and estimated in the push stage, which can be utilized to conduct the parameter setting for the constraint-handling approaches to be applied in the pull stage. Then, a modified form of a constrained multi-objective evolutionary algorithm (CMOEA), with improved epsilon constraint-handling, is applied to pull the infeasible individuals achieved in the push stage to the feasible and non-dominated regions. To evaluate the performance regarding convergence and diversity, a set of benchmark CMOPs and a real-world optimization problem are used to test the proposed PPS (PPS-MOEA/D) and state-of-the-art CMOEAs, including MOEA/D-IEpsilon, MOEA/D-Epsilon, MOEA/D-CDP, MOEA/D-SR, C-MOEA/D and NSGA-II-CDP. The comprehensive experimental results show that the proposed PPS-MOEA/D achieves significantly better performance than the other six CMOEAs on most of the tested problems, which indicates the superiority of the proposed PPS method for solving CMOPs.

Comparison between MOEA/D and NSGA-III on a set of novel many and multi-objective benchmark problems with challenging difficulties

Abstract: Currently, evolutionary multiobjective optimization (EMO) algorithms have been successfully used to find a good approximation of many-objective optimization problems (MaOPs). To measure the performance of EMO algorithms, many benchmark multiobjective test problems have been constructed. Among them, DTLZ and WFG are two representative test suites with the scalability to the number of variables and objectives. It should be pointed out that MaOPs can be more challenging if they are involved with difficult problem features, such as objective scalability, complicated Pareto set, bias, disconnection, or degeneracy. In this paper, a set of ten new test problems with above-mentioned difficulties are constructed. Some experimental results on these test problems found by two popular EMO algorithms, i.e., MOEA/D and NSGA-III, are reported and analyzed. Moreover, the performance of these two EMO algorithms with different population sizes on these test problems are also studied.

Adaptive Epsilon dominance in decomposition-based multiobjective evolutionary algorithm

Abstract: Complicated geometric shapes of Pareto fronts can cause difficulties for multiobjective evolutionary algorithms. To deal with these difficulties, efficient diversity strategies must be highly addressed in order to obtain a set of representative Pareto solutions. In decomposition-based multiobjective evolutionary algorithms, this is often done by optimizing multiple single objective subproblems defined by a set of weight vectors. For complicated Pareto fronts with extreme convexity, disconnection or degeneracy, however, it is nontrivial to set these weight vector properly. To overcome this shortcoming, we propose a new decomposition-based multiobjective evolutionary algorithm based on a hybrid weighting strategy, which optimizes both random subproblems and fixed subproblems. To maintain diversity of nondominated solutions stored in external population, a new archiving strategy based on adaptive Epsilon dominance is also suggested in our proposed algorithm. Our experimental results have showed that our proposed algorithm is superior to several other state-of-the-art multiobjective evolutionary algorithms on a set of benchmark multiobjective test problems with different challenging difficulties regarding the geometric shapes of Pareto fronts.

Variable-Length Pareto Optimization via Decomposition-Based Evolutionary Multiobjective Algorithm

Abstract: Optimization problems with variable-length decision space are a class of challenging optimization problems derived from some real-world applications, such as the composite laminate stacking problem and the sensor coverage problem. Unlike other optimization problems, the solutions in these problems might be represented as the vectors with different variable size (i.e., dimensionality). So far, some research efforts have been done on the use of evolutionary algorithms (EAs) for solving single objective variable-length optimization problems. In fact, the variable-length problem difficulty can also exist in multiobjective optimization. However, such challenging problems have not yet gained much attention in the area of evolutionary multiobjective optimization. To facilitate the research on the variable-length Pareto optimization, we first suggest a systematic toolkit for constructing benchmark multiobjective test problems with variable-length feature in this paper. Then, we also propose a variable-length multiobjective EA based on a two-level decomposition strategy, which decomposes a multiobjective optimization problem in terms of the penalty boundary intersection search directions and the dimensionality of variables. The performance of our proposed algorithm and the other three state-of-the-art algorithms on these problems are compared. To further show the effectiveness of our proposed algorithm, some experimental results on a bi-objective laminate stacking optimization problem are also reported and analyzed.

A Multiobjective Approach Based on Gaussian Mixture Clustering for Sparse Reconstruction

Abstract: The application of multiobjective approaches for sparse reconstruction is a relatively new research topic in the area of compressive sensing. Unlike conventional iterative thresholding methods, multiobjective approaches attempt to find a set of solutions called Pareto front (PF) with different sparsity levels. The major focus of the existing sparse multiobjective approaches is to find the knee region of PF, where the K-sparse solution should reside. However, the strategies in these approaches for finding the knee region of PF are not very reliable due to the sensitivities on the setting of control parameters or noise levels. In this paper, we propose a new strategy based on Gaussian mixture models (GMMs) within a decomposition-based multiobjective framework for sparse reconstruction. The basic idea is to cluster the population found by a chain-based search procedure into two subsets via GMM. One of them with the small values of loss function should include the knee region. Our proposed algorithm was tested on a set of six artificial instance sets at four different noise levels. The experimental results showed that our proposed algorithm is superior to two existing sparse multiobjective approaches and one iterative thresholding algorithm.

Design of Closed-Loop Parameters With High Dynamic Performance for Micro-Grating Accelerometer

Abstract: A novel closed-loop parameter design scheme with timing sequence control method is proposed to obtain fast tracking ability of micro-grating accelerometer. Firstly, we investigate a timing sequence control method to achieve the minimum time-delay of signal processing only dependent on the response capability of sensing element, which is the same as half modulation period in the designed closed-loop accelerometer system. Considering optical sensing principle and time-delay of signal processing, we establish a dynamic equation for closed-loop micro-grating accelerometer. Then, we analyze the design principle of closed-loop parameters on the condition of system stability, which describes the relationship between time-delay and control parameters to guarantee fast tracking performance of micro-grating accelerometer system. And, through proposed parameter design principle, the closed-loop parameters of micro-grating accelerometer can be obtained with the gains of forward channel and feedback channel in closed-loop detection system. The conducted experiment results show that micro-grating accelerometer can achieve the nonlinearity within 0.28%, the fast step response time of 0.8ms and -3dB bandwidth up to 525Hz, which validate the effectiveness of our parameter design scheme and timing sequence control method.

Adjustment of Weight Vectors of Penalty-Based Boundary Intersection Method in MOEA/D

Abstract: Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) is one of the dominant algorithmic frameworks for multi-objective optimization in the area of evolutionary computation. The performance of multi-objective algorithms based on MOEA/D framework highly depends on how a diverse set of single objective subproblems are generated. Among all decomposition methods, the Penalty-based Boundary Intersection (PBI) method has received particular research interest in MOEA/D due to its ability for controlling the diversity of population for many-objective optimization. However, optimizing multiple PBI subproblems defined via a set of uniformly-distributed weight vectors may not be able to produce a good approximation of Pareto-optimal front when objectives have different scales. To overcome this weakness, we suggest a new strategy for adjusting weight vectors of PBI-based subproblems in this paper. Our experimental results have shown that the performance of MOEA/D-PBI with adjusted weight vectors is competitive to NSGA-III in diversity when dealing with the scaled version of some benchmark multi-objective test problems.

An Adaptive Parameter Tuning Strategy for Many-objective Evolutionary Algorithm

Abstract: Decomposition-based multi-objective evolutionary algorithm has been acknowledged as a promising paradigm for multi-objective optimization problems. Nevertheless, its performance deteriorates seriously when the number of objectives increases. To improve its performance, generating high-quality solution is vital. Acknowledging the success of hybridizing different recombination operators, a selection strategy to choose from a set of differential evolution (DE) operators is adopted in this paper. The selection strategy could combine the advantages of these DE operators. Yet, the performance of DE operators depends highly on their control parameters, which should be tuned adaptively along the search process to fully explore their search abilities. An adaptive parameter tuning strategy is hereby proposed by estimating a Cauchy and a normal distribution from history information for the control parameters, respectively. Experimental comparison using DTLZ1-DTLZ4, with the number of objectives ranging from three to ten, is carried out between six state-of-the-art algorithms and the developed algorithm. Empirical results justify the outperformance of the developed algorithm against the compared algorithms in terms of some commonly-used performance metrics.

High bandwidth signal detection method for tracking resonant frequency of integrated optical waveguide gyro

Abstract: The invention discloses a high bandwidth signal detection method for tracking the resonant frequency of an integrated optical waveguide gyro, and belongs to the technical field of integrated optical waveguide gyro. The method comprises the steps of: firstly, establishing a working circuit structure of a dual-tracking system, modulating a laser signal through an OSB modulator and an integrated optical modulator, and calculating the frequency of input light in a ring waveguide resonant cavity; meanwhile, demodulating output light into a low frequency component and a high frequency component, transmitting the low frequency component to a low frequency controller, and transmitting the low frequency component to a laser after feedback; outputting and converting the high frequency component intoa cosine signal after passing through a high frequency controller, and driving an MZI to lock the resonant frequency; and finally, controlling, by the low frequency controller, the laser frequency tochange within a wide range, quickly tracking, by the high frequency controller, the change of the resonant frequency within a small tracking range, and ensuring that a closed-loop error of resonant frequency tracking in a synchronization process is zero. According to the high bandwidth signal detection method disclosed by the invention, by means of the cooperative work of high and low frequency control loops, simultaneous detection and tracking control of the high and low frequency components are ensured, and high-precision detection is ensured while improving the measurement bandwidth.

Practical reflective birefringent fiber interferometer sensor.

Abstract: This paper proposes a simple and practical reflective birefringent fiber interferometer sensor that consists of a polarization beam splitter, a polarization-maintaining transmission fiber, and a sensor unit comprising two segments of birefringent fibers and a thin-film reflector. This sensor could be used for dual parameter sensing. Experiments with different temperatures and lateral loads applied to the sensor unit demonstrated temperature and load sensitivity of ∼0.0010 nm/°C and 0.298 nm/N, respectively. Further study showed that temperature-insensitive lateral load measurement can be achieved by using equal length of birefringent fibers and performing differential wavelength measurement. The sensor is robust against environmental disturbance on the transmission fiber, making it potentially attractive for practical field applications.

Practical temperature-insensitive pressure sensor based on reflective birefringence fiber interferometer

Abstract: This paper proposes and demonstrates a practical temperature-insensitive pressure sensor based on the simple reflective birefringence fiber interferometer which consists of only a polarization beam splitter (PBS), two segments of solid core polarization-maintaining photonic crystal fibers (PM-PCFs) with film reflector deposited on the far end of one of the PM-PCFs. We derived its spectrum response equation and the practical temperature insensitive lateral pressure sensor was designed and demonstrated experimentally.