Neighbor-Induction and Population-Dispersion in Differential Evolution Algorithm

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA) . This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, differential evolution algorithm, and Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with Nash–Sutcliffe Efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.

Hybrid metaheuristic algorithms have become popular for solving complex problems that are very challenging to overcome with conventional methods in recent years. This paper presents a hybrid algorithm called DE-ACO that combines Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithms. The main idea is to use the Differential Evolution (DE) algorithm for a good starting point for Ant Colony Optimization (ACO) algorithm. A benchmark of six well-known test functions is employed to check the performances of the proposed approach in terms of convergence rate, quality of optimum solutions and computing time. The results obtained show that the performances of the hybrid algorithm outperform significantly the DE and ACO algorithms.

The Differential Evolution (DE) algorithm, under the family of Evolutionary Algorithms (EAs), is one of the powerful algorithms used for solving continuous parameter optimization challenges. The simplistic nature and robustness of the classical DE algorithm have drawn researchers' attention towards its progressive enhancement. This work reports on an investigation of the behavioral changes of the classical DE algorithm, evoked when its mutation and crossover components are fine tuned for enhancement of DE's performance. The scope of this study covers the implementation of a mutation level enhancement and a crossover level enhancement, followed by their integration. The mutation and the crossover components are augmented by incorporation of Centroid DE and Superior-Superior & Superior-Inferior DE logics, respectively. The algorithms appraised in this inquiry were classical DE, Centroid based DE(cDE), Superior-Superior based DE (ssDE), Superior-Inferior DE (siDE), Centroid Superior-Superior DE (cssDE) and Centroid Superior-Inferior DE (csiDE). These algorithms were evaluated by comparison of the values of their mean objective function (MOV), and their speed, at solving the global optimization problems in a simple benchmarking function suite with 4 functions of different categories. The study concludes that the DE algorithm shows enhancement performance with modified mutation and crossover components. However, with a trend for inconsistency for varying values of its control parameters and benchmarking problems.

In this paper, differential evolution (DE) optimization algorithm is applied to design robust power system stabilizer (PSS). The design problem of the proposed controller is formulated as an optimization problem and DE is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions; disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations.KeywordsDifferential evolutionpower system stabilizerlow frequency oscillationspower system stability

Several contemporary algorithms, including cuckoo search (CS), were applied to the CEC 2017 problem set, which includes a wide variety of 120 very difficult subproblems. We found that the algorithms were ineffective, especially when the number of dimensions was high. We configured several usage patterns of Linnik flight with the inverse of the golden ratio (1/Φ) to replace Lévy flight in CS, resulting in a new search mechanism that increased the efficiency of the CS algorithm. The impacts of each Linnik flight usage pattern were evaluated using the CEC 2017. The experimental results showed that: 1) CS variants that used Linnik flight were more capable than CS variants that used Lévy flight and 2) CS variants that used a mixture of Linnik flight and quantum-behaved mechanisms were even more capable. The primary effect of Linnik flight is the strengthening of the ranking, while that of the quantum-behaved mechanisms is a decreased error. A chaotic-initialized quantum-Linnik flight CS (CQLCS) algorithm is proposed. Among the 66 competitive methods applied to the CEC 2017, CQLCS ranked first and won the contemporary competitive algorithms section, which also included several advanced variants of differential evolution (DE) and particle swarm optimization (PSO) algorithms. The CQLCS could potentially be improved further by adjusting the probability of the occurrence of Linnik flight. The processes for building improved variants were analyzed to discover how, or if, this improvement could be achieved. Finally, the CQLCS algorithm required fewer lines of code to run than did the DE and PSO variants.

The problem of trajectory planning and obstacle avoidance in redundant robots is addressed in this paper. Four variants of Particle Swarm Optimization (PSO) and a Differential Evolution (DE) algorithm are proposed to solve this problem. Simulation experiments on a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacles are conducted. The manipulator is required to move from a start position to a goal position with minimum error while avoiding collision with the obstacles in the workspace. The performance of the proposed algorithms is compared with the results reported in the literature and the comparative results are presented. It is observed that qPSO-C performs better in free space and PSO-C performs better in environment with obstacles in terms of minimizing error average convergence time. The performance of DE improves when the number of obstacles increases.

The development and application of the differential evolution (DE) optimisation algorithm to the problem of worst-case analysis of nonlinear control laws for hypersonic re-entry vehicles is described. The algorithm is applied to the problem of evaluating a proposed nonlinear handling qualities clearance criterion for a detailed simulation model of a hypersonic re-entry vehicle (also known as a reusable launch vehicle (RLV)) having a full-authority nonlinear dynamic inversion (NDI) flight control law. A hybrid version of the differential evolution algorithm, incorporating local gradient-based optimisation, is also developed and evaluated. Comparisons of computational complexity and global convergence properties reveal the significant benefits which may be obtained through hybridisation of the standard differential evolution algorithm. The proposed optimisation-based approach to worst-case analysis is shown to have significant potential for improving both the reliability and efficiency of the flight clearance process for next generation RLV’s.

Social learning differential evolution

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

In this work, a method to control the parameters of Differential Evolution (DE) algorithm is proposed. Here the control parameters of DE are co-evolved by Particle Swarm Optimization (PSO) algorithm. The classical DE algorithm has two main control parameters: Scale Factor (F) and Cross-over Rate (CR). These are selected on trial-and-error basis for solving optimization problems. Several optimization problems lead to optimal or sub-optimal solution by proper selection of control parameters of the DE algorithm. In this proposed method, PSO algorithm is used to tune the scale factor and cross-over rate in DE algorithm. Basically PSO algorithm is used as a meta-optimizer for DE algorithm. The proposed method is termed as mPSO-DE in this paper. The mPSO-DE algorithm is applied on 12 benchmark unconstrained optimization problems. The obtained results are compared with that of classical DE algorithm. From the experimental studies, it has been found that the proposed mPSO-DE algorithm performed better than DE algorithm.

Parallel population-based algorithm portfolios: An empirical study

A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination

Transmission rate is one of the contributing factors in the performance of wireless sensor networks. Congested network causes reduced network response time, queuing delay, and more packet loss. To address the issue of congestion, we have proposed transmission rate control methods. To avoid the congestion, we have adjusted the transmission rate at current node based on its traffic loading information. Multiclassification is done to control the congestion using an effective data science technique, namely support vector machine (SVM). In order to get less miss classification error, differential evolution (DE) and grey wolf optimization (GWO) algorithms are used to tune the SVM parameters. The comparative analysis has shown that the proposed approaches DE‐SVM and GWO‐SVM are more proficient than other classification techniques. Moreover, DE‐SVM and GWO‐SVM have outperformed the benchmark technique genetic algorithm–SVM by producing 3% and 1% less classification errors, respectively. For fault detection in wireless sensor networks, we have induced four types of faults in the sensor readings and detected the faults using the proposed enhanced random forest. We have made a comparative analysis with state of the art data science techniques based on two metrics, ie, detection accuracy and true positive rate. Enhanced random forest has detected the faults with 81% percent accuracy and outperformed the other classifiers in fault detection.

Parameter estimation of the soil water retention curve model with Jaya algorithm

The major environmental and engineering applications of the self-potential method have been investigations of subsurface water movement. Specific uses have included mapping of seepage flow associated with dams, dikes, reservoir floors, and other containment structures; studies of general groundwater movement; and delineation of flow patterns in the vicinity of landslides, wells, faults, drainage structures, shafts, tunnels, and sinkholes. The method also has been used to a lesser extent for subsurface thermal investigations such as mapping of steam flood fronts and coal mine fires, and preliminary studies indicate that it could be used to help delineate chemical concentration gradients associated with subsurface contaminants. Self-potential anomaly amplitudes generated by such environmental and engineering sources tend to be lower than those seen in mineral and geothermal exploration, and spurious anomalies associated with the presence of man-made structures at most field sites often create very high noise levels. Therefore extreme care in the acquisition and reduction of field data, and recognition of characteristic fields associated with artificial and natural noise sources, are critical to the success of environmental self-potential investigations. Interpretation of self-potential data often is qualitative, using visual correlation between observed profile or contour patterns and known or suspected seepage flow paths or other sources. The use of available geometric source models, including charged points, lines, spheres, cylinders, and sheets, can provide useful first estimates of source depth and configuration. Computer programs based on analytical modeling techniques can be used to calculate self-potentials generated by subsurface gradients of pressure, temperature, or chemical concentration. If in-situ physical property parameters such as cross-coupling coefficients and hydraulic, thermal, and electrical conductivity are known or can be reasonably estimated, these techniques can provide useful information about the nature, location, and strength of self-potential sources.