Premature Convergence Phenomenon Research Articles

Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

An enhanced dung beetle optimization algorithm (EDBO) is proposed for nonlinear optimization problems with multiple constraints in manufacturing. Firstly, the dung beetle rolling phase is improved by removing the worst value interference and coupling the current solution with the optimal solution to each other, while retaining the advantages of the original formulation. Subsequently, to address the problem that the dung beetle dancing phase focuses only on the information of the current solution, which leads to the overly stochastic and inefficient exploration of the problem space, the globally optimal solution is introduced to steer the dung beetle, and a stochastic factor is added to the optimal solution. Finally, the dung beetle foraging phase introduces the Jacobi curve to further enhance the algorithm’s ability to jump out of the local optimum and avoid the phenomenon of premature convergence. The performance of EDBO in optimization is tested using the CEC2017 function set, and the significance of the algorithm is verified by the Wilcoxon rank-sum test and the Friedman test. The experimental results show that EDBO has strong optimization-seeking accuracy and optimization-seeking stability. By solving four engineering optimization problems of varying degrees, EDBO has proven to have good adaptability and robustness.

Hybrid optimization algorithm for estimating soil parameters of spoil hopper deposition model for trailing suction hopper dredgers

Trailing suction hopper dredger is a kind of hydraulic dredger, it has the characteristics of self-propelled, selfloading, self-dredging, self-unloading, it is the main force in dredging and blowing works, it is widely used in the world, it can be said that where there is a big dredging project where there is a trailing suction hopper dredger’s figure. The loading optimization process of trailing suction hopper dredger contains a lot of dredging parameters related to soil type, and the soil type under different working conditions is not very clear. In this study, we present a hybrid optimization technique based on simulated annealing and multi-population genetic algorithm to enhance the loading efficiency of a trailing suction hopper dredger and to examine the variation of dredged soil parameters. The soil parameters of the spoil hopper deposition model were estimated using this hybrid optimization algorithm. The experimental results show that the soil parameters are successfully estimated and verified by our measured construction data of a trailing suction hopper dredger. In addition, our proposed method has the highest accuracy of soil parameter estimation, the fastest algorithm convergence, and excellent robustness compared to the other three intelligent optimization methods. In addition, our method successfully avoids the phenomenon of premature convergence that usually occurs in traditional genetic algorithms, and the parameters show strong adaptability to different vessels under the same dredging area.

Design of Nonlinear Active Disturbance Rejection Controller Based on the Adaptive Particle Swarm Optimization Algorithm for the Maglev Train Traction Control System

This paper focuses on speed tracking control of the maglev train operation system. Given the complexity and instability of the maglev train operation system, traditional speed‐tracking control algorithms demonstrate poor tracking accuracy and large tracking errors. The maglev train is easily affected by external interference, increasing train energy consumption and reducing passengers’ riding comfort. This study proposes a control algorithm called APSO‐NLADRC to address the deficiencies of the automatic train operation control algorithms. The APSO‐NLADRC is based on adaptive particle swarm optimization (APSO) algorithm parameter optimization nonlinear active disturbance rejection controller (NLADRC). The method of population comparison, linear update of learning factors, and adaptive updating of inertia weight values addresses the premature convergence phenomenon that occurs during the parameter optimization of the traditional particle swarm algorithm. The APSO algorithm solves the problem that the parameters of the NLADRC are difficult to adjust. Compared with PID, NLADRC, and NLADRC based on traditional particle swarm optimization algorithms, the proposed control algorithm has higher tracking accuracy and more robust anti‐interference capability and provides better comfort.

An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization

In view of the shortcomings such as slow search speed, low optimization precision and premature convergence of artificial hummingbird algorithm, an enhanced artificial hummingbird algorithm based on golden sine factor named DGSAHA is proposed. Firstly, chaos mapping is used to generate the initial candidate solution to increase the diversity of the population, which lays the foundation for the global search. Then, perturb the individuals by means of the differential variation between individuals on the group, thereby enhancing the diversity of the population, preserving the excellent individuals, eliminating the inferior individuals, and guiding the search process to approach the global optimal solution, avoiding the phenomenon of premature convergence. Finally, the golden sine factor were introduced in the guided foraging stage is conducive to the full exploration of the global optimal solution, reducing the search space for individuals to approach the optimal solution. And, it facilitates the balance between “exploration” and “exploitation” of algorithm. Thereby, the accuracy and speed of the DGSAHA can be improved to a certain extent. 25 classic functions, the CEC2014 and CEC2019 benchmark functions were tested, and several representative meta-heuristic algorithms and its improved algorithm are compared for evaluate the validity of DGSAHA. Meanwhile, the dimensional scalability of the variable-dimensional test function is discussed. The results of non-parametric statistical analysis and performance index show that DGSAHA in this paper has better comprehensive optimization performance, significantly improves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. Finally, the performance of DGSAHA and the practicability of truss structure are answered by three engineering examples of plane and space truss topology optimization problem. This optimization problem considers not only the static constraints such as stress, displacement and buckling, but also the dynamic constraints of frequency and motion stability. In order to avoid singularity and unnecessary analysis, the stiffness, mass and load matrices are reconstructed in finite element analysis. A lighter truss structure than the existing solution is obtained. The validity, extensibility and practicability of the algorithm are further illustrated.

An efficient differential evolution algorithm based on orthogonal learning and elites local search mechanisms for numerical optimization

Differential evolution (DE) is an efficient stochastic algorithm for solving global numerical optimization problems. To effectively relieve the stagnation and premature convergence problems in DE, this paper presents an efficient DE variant, abbreviated as OLELS-DE, by designing orthogonal learning and elites local search mechanisms. More specifically, the stagnation or premature convergence phenomenon will be detected by monitoring the best individual’s update condition during the evolution, then a population diversity estimation technique is utilized to distinguish between these two conditions empirically. To recover the population’s evolution vitality according to the classification results, the enhanced orthogonal learning scheme is employed by selecting two different groups of individuals for constructing the orthogonal experimental design procedure. Moreover, the elites local search method is developed by selecting several well-performing elite individuals based on the Gaussian distribution model to further assist the former orthogonal learning mechanism. This scheme is designed to enhance the exploitation ability by searching the regions around elite individuals. The parameters and strategies in above two mechanisms are designed on the expectation of balancing the local exploitation and global exploration capabilities. The optimization performance of proposed OLELS-DE algorithm is evaluated based on 30 benchmark functions from CEC2014 test suite and is compared with eight state-of-the-art DE variants. As it was anticipated, the incorporation of orthogonal learning and elites local search mechanisms helps OLELS-DE have significantly better or at least comparable performance to the adopted DE competitors.

Colliding bodies optimization with Morlet wavelet mutation and quadratic interpolation for global optimization problems

This paper represents a new variant of colliding bodies optimization (CBO) and the objective is to alleviate the lack of population diversity, premature convergence phenomenon, and the imbalance between the diversification and intensification of the CBO method. The CBO is a meta-heuristic algorithm based on momentum and energy laws in a one-dimensional collision between two bodies. The proposed method is designed by hybridization of the CBO with Morlet wavelet (MW) mutation and quadratic interpolation (QI) (MWQI-CBO). The Morlet wavelet mutation is employed to improve the CBO so that it can explore the search space more effectively on reaching a better solution. Besides, quadratic interpolation that utilized historically best solution is added to CBO to enhance the exploitation phase. Two new parameters are defined to have a better balance between the diversification and the intensification inclinations. The proposed algorithm is tested in 24 mathematical optimization problems including 30 design variables and compared with standard CBO and some state-of-art metaheuristics. Besides, the optimal design of five standard discrete and continuous structural design problems with various constraints such as strength, stability, displacement, and frequency constraints are studied. It is found that MWQI-CBO is quite competitive with other meta-heuristic algorithms in terms of reliability, solution accuracy, and convergence speed.

An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

Remote sensing of water quality based on HJ-1A HSI imagery with modified discrete binary particle swarm optimization-partial least squares (MDBPSO-PLS) in inland waters: A case in Weishan Lake

Remote sensing has been recognized as an effective tool to monitor water quality in inland waters. An adaptive model coupled with a modified discrete binary particle swarm optimization algorithm utilizing the catastrophe strategy and partial least squares (MDBPSO-PLS) was developed to retrieve the water quality indexes (chlorophyll a (chl-a), total suspended matter (TSM), and turbidity) in Weishan Lake. Based on the selective bands for water quality retrieval, the proposed MDBPSO algorithm was compared with original DBPSO and Genetic Algorithm (GA) in order to validate the feasibility and efficiency of the proposed algorithm. The comparison results indicated that MDBPSO with the catastrophe strategy could avoid the premature convergence phenomenon of DBPSO algorithm. The hyperspectral data from HJ-1A Hyperspectral Imager (HSI) selected by MDBPSO were utilized to establish the PLS model by correlating the spectral data with measured water quality parameters. Then the established PLS model was compared with the PLS model established with full spectral data and measured water quality parameters. MDBPSO-PLS showed the better performance in retrieving the above-mentioned three parameters in Weishan Lake. The comprehensive errors between measured and retrieved parameters in MDBPSO-PLS (chl-a (R2 = 0.97, CE = 3.16%), TSM (R2 = 0.97, CE = 5.84%), and turbidity (R2 = 0.97, CE = 7.40%)) were lower than those in PLS model (chl-a (R2 = 0.55, CE = 18.71%), TSM (R2 = 0.49, CE = 49.50%), and turbidity (R2 = 0.50, CE = 50.46%). The three water quality parameters were mapped by using MDBPSO-PLS. Our observations indicated that the MDBPSO-PLS model showed the significantly higher retrieval accuracy than the PLS model and could provide realistic information on the distribution of chl-a, TSM, and turbidity in Weishan Lake based on HJ-1A HSI.

Application of Improved APO Algorithm in Vulnerability Assessment and Reconstruction of Microgrid

Artificial Physics Optimization (APO) has good global search ability and can avoid the premature convergence phenomenon in PSO algorithm, which has good stability of fast convergence and robustness. On the basis of APO of the vector model, a reactive power optimization algorithm based on improved APO algorithm is proposed for the static structure and dynamic operation characteristics of microgrid. The simulation test is carried out through the IEEE 30-bus system and the result shows that the algorithm has better efficiency and accuracy compared with other optimization algorithms.

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

Partial discharge localization in power transformers based on the sequential quadratic programming-genetic algorithm adopting acoustic emission techniques

Partial discharge (PD) in power transformers is one of the prime reasons resulting in insulation degradation and power faults. Hence, it is of great importance to study the techniques of the detection and localization of PD in theory and practice. The detection and localization of PD employing acoustic emission (AE) techniques, as a kind of non-destructive testing, plus due to the advantages of powerful capability of locating and high precision, have been paid more and more attention. The localization algorithm is the key factor to decide the localization accuracy in AE localization of PD. Many kinds of localization algorithms exist for the PD source localization adopting AE techniques including intelligent and non-intelligent algorithms. However, the existed algorithms possess some defects such as the premature convergence phenomenon, poor local optimization ability and unsuitability for the field applications. To overcome the poor local optimization ability and easily caused premature convergence phenomenon of the fundamental genetic algorithm (GA), a new kind of improved GA is proposed, namely the sequence quadratic programming-genetic algorithm (SQP-GA). For the hybrid optimization algorithm, SQP-GA, the sequence quadratic programming (SQP) algorithm which is used as a basic operator is integrated into the fundamental GA, so the local searching ability of the fundamental GA is improved effectively and the premature convergence phenomenon is overcome. Experimental results of the numerical simulations of benchmark functions show that the hybrid optimization algorithm, SQP-GA, is better than the fundamental GA in the convergence speed and optimization precision, and the proposed algorithm in this paper has outstanding optimization effect. At the same time, the presented SQP-GA in the paper is applied to solve the ultrasonic localization problem of PD in transformers, then the ultrasonic localization method of PD in transformers based on the SQP-GA is proposed. And localization results based on the SQP-GA are compared with some algorithms such as the GA, some other intelligent and non-intelligent algorithms. The results of calculating examples both stimulated and spot experiments demonstrate that the localization method based on the SQP-GA can effectively prevent the results from getting trapped into the local optimum values, and the localization method is of great feasibility and very suitable for the field applications, and the precision of localization is enhanced, and the effectiveness of localization is ideal and satisfactory.

Quantum Particle Swarm Optimization Algorithm Based on Dynamic Adaptive Search Strategy

The particle swarm system simulates the evolution of the social mechanism. In this system, the individual particle representing the potential solution flies in the multidimensional space in order to find the better or the optimal solution. But because of the search path and limited speed, it's hard to avoid local best and the premature phenomenon occurs easily. Based on the uncertain principle of the quantum mechanics, the global search ability of the quantum particle swarm optimization (QPSO) algorithms are better than the particle swarm optimization algorithm (PSO). On the basis of the fundamental quantum PSO algorithm, this article introduces the grouping optimization strategy, and meanwhile adopts the dynamic adjustment, quantum mutation and possibility acceptance criteria to improve the global search capability of the algorithm and avoid premature convergence phenomenon. By optimizing the test functions, the experimental simulation shows that the proposed algorithm has better global convergence and search ability.

Democratic PSO for truss layout and size optimization with frequency constraints

This paper represents a new algorithm for structural optimization with frequency constraints. The new algorithm is termed Democratic Particle Swarm Optimization. The emphasis is placed upon alleviating the premature convergence phenomenon which is believed to be one of flaws of the original PSO. When considering frequency constraints in a structural optimization problem, the search spaces happen to be highly non-linear and non-convex hyper-surfaces with numerous local optima and naturally the problem of premature convergence is amplified. The proposed algorithm is capable of coping with this problem. Four numerical examples are presented to demonstrate the viability of the algorithm.

Using hybrid APPM to solve Lennard-Jones cluster problems

Lennard-Jones LJ cluster is one important problem in chemistry, materials and physics. The main difficulty is the amount of local optima. Recently, an Artificial Plant Photosynthesis and Phototropism Mechanism APPM is proposed which simulates the plant growing process. In this paper, APPM is applied to solve LJ cluster problem. To avoid the premature convergence phenomenon, a new strategy, Limited memory Broyden-Fletcher-Goldfarb-Shanno L-BFGS is employed to increase the local search efficiency. Furthermore, seed technology is also introduced to slow the problem dimension. Simulation results show this new hybrid algorithm is effective for LJ2-LJ17 when compared with other three stochastic algorithms.

A Particle Swarm Optimization with Variety Factor

A new particle swarm optimization (PSO) algorithm (a PSO with Variety Factor, VFPSO) based on the PSO was proposed. Compared with the previous algorithm, the proposed algorithm is to update the Variety Factor and to improve the inertia weight of the PSO. The target of the improvement is that the new algorithm could go on enhancing the robustness as before and should reduce the risk of premature convergence. The simulation experiments show that it has great advantages of convergence property over some other modified PSO algorithms, and also avoids algorithm being trapped in local minimum effectively. So it can avoid the phenomenon of premature convergence.

A Particle Swarm Optimization Based on Evolutionary Game Theory for Discrete Combinatorial Optimization

This paper presented a new particle swarm optimization based on evolutionary game theory (EPSO) for the traveling salesman problem (TSP) to overcome the disadvantages of premature convergence and stagnation phenomenon of traditional particle swarm optimization algorithm (PSO). In addition ,we make a mapping among the three parts discrete particle swarm optimization (DPSO)、 evolutionary game theory and traveling salesman problem by using replicator dynamics to restrict the behavior of particles.. Finally, we give experimental examples in the standard library LIBTSP and the performance analyses of the algorithm. Comparing with the genetic algorithm and basic particle swarm optimization algorithm we show that the EPSO algorithm is effective.

Optimization Problem of Maintenance Resources Allocation Based on Partical Swarm Optimization

In the communications maintenance support system, how to progress the optimal allocation of maintenance resources is key to design the system. For Optimization Problem of Maintenance Resources Allocation, this paper analysis the phenomenon of premature convergence in Partical Swarm Optimization(PSO), and proposed an improved Particle Swarm Optimization. The two examples study how to use the improved PSO to solve Optimization Problem of Maintenance Resources and validate an improved PSO.

The fitness evaluation strategy in particle swarm optimization

The particle swarm optimization (PSO) computational method has recently become popular. However, it has limitations. It may trap into local optima and cause the premature convergence phenomenon, especially for multimodal and high-dimensional problems. In this paper, we focus on investigating the fitness evaluation in terms of a particle’s position. Particularly, we find that the fitness evaluation strategy in the standard PSO has two drawbacks, i.e., “two steps forward and one step back” and “two steps back and one step forward”. In addition, we propose a general fitness evaluation strategy (GFES), by which a particle is evaluated in multiple subspaces and different contexts in order to take diverse paces towards the destination position. As demonstrations of GFES, a series of PSOs with GFES are presented. Experiments are conducted on several benchmark optimization problems. The results show that GFES is effective at handling multimodal and high-dimensional problems.

유전자알고리즘의 성능향상을 위한 선택적 돌연변이

Since the premature convergence phenomenon of genetic algorithms (GAs) degrades the performances of GAs significantly, solving this problem provides a lot of effects to the performances of GAs. In this paper, we propose a selective mutation method in order to improve the performances of GAs by alleviating this phenomenon. In the selective mutation, individuals are additionally mutated at the specific region according to their ranks. From this selective mutation, individuals with low ranks are changed a lot and those with high ranks are changed small in the phenotype. Finally, some good individuals search around them in detail and the other individuals have more chances to search new areas. This results in enhancing the performances of GAs through alleviating of the premature convergence phenomenon. We measured the performances of our method with four typical function optimization problems. It was found from experiments that our proposed method considerably improved the performances of GAs.

A Modified Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is a new optimization algorithm, which is applied in many fields widely. But the original PSO is likely to cause the local optimization with premature convergence phenomenon. By using the idea of simulated annealing algo-rithm, we propose a modified algorithm which makes the most optimal particle of every time of iteration evolving continu-ously, and assign the worst particle with a new value to increase its disturbance. By the testing of three classic testing functions, we conclude the modified PSO algorithm has the better performance of convergence and global searching than the original PSO.