Hybrid Differential Evolution Particle Swarm Research Articles

Hybrid differential evolution particle swarm optimization (DEA-DCWPSO) for resource-constrained multi-project scheduling problem

In this paper, we studied the resource constrained project scheduling problem, and the research object is extended to the multi-project environment. On the basis of multi-project priority evaluation, with the goal of minimizing the weighted duration of multi-project, a multi-project schedule planning model is constructed. Through reasonable scheduling of multiple parallel projects sharing resources under resource constraints, it provides a decision-making basis for project managers to allocate resources reasonably under resource constraints to meet the duration requirements of each project and to shorten the weighted total duration of multiple projects as much as possible. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist Retention (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of particle swarm optimization algorithm with dynamically changing inertia weight. Through the dynamic changing inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can be used to solve the RCMPSP model more effectively.

Hybrid Differential Evolution Particle Swarm Optimization Algorithm for Solving Resource Leveling Problem of Multi-project with Fixed Duration

This paper attempts to substitute Resource Leveling Problem (RLP) into multi-project environment and construct Resource Leveling Problem of Multi-project (RLPMP) model with the goal of minimizing the sum of weighted mean square deviations of multi-resource requirements. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist reservation (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of Particle Swarm Optimization (PSO) algorithm with dynamic inertia weight. Through the dynamic change of inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can effectively solve the RLPMP model, and then effectively improve the balance of multi-project resources.

Differential evolution particle swarm optimization algorithm based on good point set for computing Nash equilibrium of finite noncooperative game

In this paper, a hybrid differential evolution particle swarm optimization (PSO) method based on a good point set (GPDEPSO) is proposed to compute a finite noncooperative game among N people. Stochastic functional analysis is used to prove the convergence of this algorithm. First, an ergodic initial population is generated by using a good point set. Second, PSO is proposed and utilized as the variation operator to perform variation crossover selection with differential evolution (DE). Finally, the experimental results show that the proposed algorithm has a better convergence speed, accuracy, and global optimization ability than other existing algorithms in computing the Nash equilibrium of noncooperative games among N people. In particular, the efficiency of the algorithm is higher for determining the Nash equilibrium of a high-dimensional payoff matrix game.

Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization

The filter is an important building block of modern communication and electronic systems. Based on well-defined bandwidth, it extracts the desired portion of the spectrum when the raw input signal is applied. Efficacy of filtering depends on the preciseness of bandwidth to avoid co-channel interference and signal loss. In addition, it must provide higher stopband attenuation and passband attenuation very close to unity with a tolerable quantity of pass/stop band ripple. Design/implementation of sharp edge modern FIR filter is structured as a multi-objective, constrained, complex, and highly nonlinear (hence multimodal) error minimization challenge. Hence, this work proposes a novel objective (normalized error fitness) function and a robust hybrid algorithm for the effective searching of the optimal filter coefficients for providing excellent sharp edge frequency response during the filtering action. Most popular particle swarm optimization (PSO) and differential evolution (DE) algorithm are effectively combined together to frame the proposed hybrid DE-PSO algorithm for enhancing the exploration and exploitation abilities of it. The proposed hybrid algorithm is validated using twelve different benchmark functions. Through simulations, the qualitative performance of the proposed approach is compared with the conventional PSO, DE, real-coded genetic algorithm and the Parks–McClellan method.

Optimal design of fuzzy PID controller for deregulated LFC of multi-area power system via mine blast algorithm

This paper presents a novel optimal fuzzy proportional–integral–derivative (fuzzy PID) controller for load frequency control (LFC) designed by a proposed approach of mine blast algorithm (MBA) for multi-interconnected areas. The system includes reheat thermal connected power systems with the effect of the governor dead zone and turbine generation rate constraint nonlinearity. The proposed approach is used to determine the optimal parameters of the fuzzy PID controller to minimize the integral time absolute error. The proposed controller is inserted in multi-interconnected power systems which are built in Simulink/MATLAB library; triangular membership function is used for fuzzy PID controller. Additionally, the optimum adjustment of the fuzzy PID controller parameters under contracted scenario for large step demands and disturbances is investigated by MBA. The obtained results are compared to those obtained via antlion optimizer, artificial bee colony, hybrid differential evolution particle swarm optimization and hybrid PSO pattern search algorithm. The obtained results confirmed the superiority of the proposed MBA in designing the fuzzy PID-LFC as it provides less error with best statistical parameters compared to the others.

Hybrid differential evolution particle swarm optimisation optimised fuzzy proportional–integral derivative controller for automatic generation control of interconnected power system

A novel fuzzy proportional–integral derivative (PID) controller is proposed in this study for automatic generation control (AGC) of interconnected power systems. The optimum gains of the proposed fuzzy PID controller are optimised employing a hybrid differential evolution particle swarm optimisation (DEPSO) technique using an integral of time multiplied by absolute value of error criterion. The superiority of hybrid DEPSO algorithm over differential evolution and particle swarm optimisation (PSO) algorithm has also been demonstrated. The results are also compared with some recently published approaches such as artificial bee colony and PSO based proportional–integral/PID controllers for the same interconnected power systems. Furthermore, performance of the proposed system is analysed by varying the different parameters such as loading condition, system parameters and objective functions. It is observed that the optimum gains of the proposed fuzzy PID controller need not be reset even if the system is subjected to variation in loading condition and system parameters. Finally, the study is extended to a three area system considering generation rate constraint to demonstrate the ability of the proposed approach to cope with multiple interconnected systems. Comparison with previous AGC methods reported in the literature validates the significance of the proposed approach.

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.

Optimisation of Tubular Hydroforming Processes for Wrinkling and Thinning Control

This paper is focused on the development and implementation of an innovative optimisation algorithm for the wrinkling and thinning control during the hydroforming of complex metallic parts. A straightforward numerical algorithm for simultaneous tracking and evaluation of the initiation of wrinkling/thinning defects was implemented, together with a numerical simulation program based on the Finite Element Method (FEM). After undesirable wrinkling/thinning patterns are identified during FEM simulation, the developed optimisation procedure (called Hybrid Differential Evolution Particle Swarm Optimisation - HDEPSO) is responsible to automatically correct the process input parameters, in order to achieve the successful forming of the desired part. In the end, the combined procedure (optimisation methodology + FEM) proved to be able to lead to a suitable numerical simulation and design tool for industrial hydroformed metallic tubular parts.

Elasto-plastic buckling of integrally stiffened panels (ISP): An optimization approach for the design of cross-section profiles

In this work, three optimization algorithms—Levenberg–Marquardt, simulated annealing and a newly developed and proposed hybrid differential evolution particle swarm optimization—are considered in the shape optimizations of cross-sections of integrally stiffened panels (ISP) for aeronautical applications, when subjected to buckling deformation modes within the elasto-plastic range. The proposed algorithm is shown to be robust and more effective, in the tested examples, than conventional optimization algorithms, leading to optimum designs of ISP cross-sections for a pre-defined buckling load-carrying capacity, combined with elasto-plastic nonlinear behaviors.