Hybrid Differential Evolution-Particle Swarm Optimization Research Articles

Hybrid-Metaheuristics Based Inverse Groundwater Modelling to Estimate Hydraulic Conductivity in a Nonlinear Real-Field Large Aquifer System

In the inverse groundwater modelling problems, the objective functions generally used contain several local minima which render the conventional gradient-based optimization unsuitable for such problems. The recently used individual population-based evolutionary methods such as differential evolution (DE) algorithm and particle swarm optimization (PSO) are often observed to get stuck into sub-optimal solution. In this study to address this issue, a hybrid- metaheuristic Differential Evolution- Particle Swarm Optimization (DE-PSO) is proposed to obtain aquifer parameters. PSO introduces a perturbation in each generation to increase the diversity in the population of DE to improve its fitness value. The developed hybrid DE-PSO optimization is coupled with finite element method (FEM) based simulator to get a simulation-optimization (SO) model. Initially, the proposed SO model is tested on a synthetic irregular domain problem to estimate aquifer transmissivity values which are compared with available zonation pattern values. Later, the SO model is applied to the Mahi Right Bank Canal (MRBC) heterogeneous unconfined aquifer system and the optimally obtained results are compared with the DE, PSO and genetic algorithm (GA) methods respectively. The performance of the hybrid DE-PSO model is also tested using various fit- independent statistics for the reliability and accuracy. The results of this study show that the hybrid-metaheuristic based DE-PSO optimization algorithm is an efficient and robust tool for inverse groundwater problem of estimating the aquifer parameters.

Optimization based Design of Dual Input PSS for Improving Small Signal Stability of Power System with RESs

This paper proposes a method to enhance the small signal stability performance of power system considering high RESs penetration. A hybrid differential evolution-particle swarm optimization (DE-PSO) is used to design and tune DIPSS parameters. Eigenvalue, damping performance, and time domain simulation are thoroughly investigated to analyze the system performance using DIPSS based on hybrid DE-PSO and find how much RESs penetration level can be considered. From the simulation results, it is found that by utilizing DIPSS based on hybrid DE-PSO can enhance the small signal stability performance of power system with significant penetration of RESs.

Hybrid Differential Evolution-Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.

Design Controller Blade Pitch Angle Wind Turbine Using Hybrid Differential Evolution Algorithm-Particle Swarm Optimization

This paper investigates the application of blade pitch angle controller to stabilize the frequency and protection again disaster of wind turbine power system. The blade pitch angle controller optimized by one of the metaheuristic algorithm called Hybrid Differential Evolution Algorithm-Particle Swarm Optimization (HDEAPSO). HDEAPSO is a hybrid algorithm between Differential Evolution Algorithm and Particle Swarm Optimization. To investigate and design the application of blade pitch angle can be done by performing the simulation using MATLAB. Simulation if performed by comparing the stability response of frequency and pitch angle at the wind turbine using conventional controller, optimized by PSO and using Hybrid Differential Evolution Algorithm-Particle Swarm Optimization (HDEAPSO). The simulation result represent that by optimizing blade pitch angle controller using Hybrid Differential Evolution-Particle Swarm Optimization can damp the frequency of wind turbine from −0.01363 pu to −0.1304 pu and reduce settling time from 46.86 second to 33.48 second when there are wind with speed 2 m/s.

Inverse Transient Radiative Analysis in Two-Dimensional Turbid Media by Particle Swarm Optimizations

Three intelligent optimization algorithms, namely, the standard Particle Swarm Optimization (PSO), the Stochastic Particle Swarm Optimization (SPSO), and the hybrid Differential Evolution-Particle Swarm Optimization (DE-PSO), were applied to solve the inverse transient radiation problem in two-dimensional (2D) turbid media irradiated by the short pulse laser. The time-resolved radiative intensity signals simulated by finite volume method (FVM) were served as input for the inverse analysis. The sensitivities of the time-resolved radiation signals to the geometric parameters of the circular inclusions were also investigated. To illustrate the performance of these PSO algorithms, the optical properties, the size, and location of the circular inclusion were retrieved, respectively. The results showed that all these radiative parameters could be estimated accurately, even with noisy data. Compared with the PSO algorithm with inertia weights, the SPSO and DE-PSO algorithm were demonstrated to be more effective and robust, which had the potential to be implemented in 2D transient radiative transfer inverse problems.

On the elasto-plastic buckling of Integrally Stiffened Panels (ISP) joined by Friction Stir Welding (FSW): Numerical simulation and optimization algorithms

Integrally Stiffened Panels (ISP) structures are composed of a base plate and one (or several) longitudinal stiffness sections of simple or complex shape. When compared to conventional reinforced structures, they present an integral profile, as the stiffened panel is obtained as a whole by means of an extrusion process. The choice of a ISP profile can lead to a more robust and defects-free solution, simultaneously maintaining the possibility to adopt complex reinforcement shapes. In the present work, three algorithms are studied and compared in the cross-section optimization of Integrally Stiffened Panels for aeronautical applications: (i) the classical Levenberg–Marquardt, (ii) the Simulated Annealing methodology, and a recently proposed approach involving (iii) a differential/nature-based algorithm called Hybrid Differential Evolution Particle Swarm Optimization (HDEPSO). Numerical simulation models are introduced for several shapes of ISP. Additionally, the presence of a Friction Stir Weld (FSW) zone is also taken into account, from the joining between distinct ISP's. This can allow us to infer about the influence of the heat affected zoned (HAZ) in the overall stability of the structure, under compressive buckling loads within the elasto-plastic regimen. Results show that the use of the proposed optimization algorithm represents a viable option, giving an added insight in terms of the buckling resistance and mechanical design in nonlinear material and geometric scenarios.