Scheduling flow shops using differential evolution algorithm

In general, power system expansion planning can be classified into three types such as generation expansion planning (GEP), transmission expansion planning (TEP) and distribution system expansion planning (DEP). Several researches have been performed in GEP and TEP. But, a huge research gap is still available in the topic of DEP. DEP can be classified into two optimization problems, namely substation expansion planning (SEP) and optimal feeder routing. This study attempts to solve real-time SEP problem for the power sector of Tamil Nadu, a state in India. Due to several design variables, the grouping of discrete and continuous variables frames the SEP problem as complicated. Hence, a promising optimization algorithm is essential to solve this complicated problem. The application of differential evolution (DE) algorithm has been presented to obtain the results with the sizes and sites of both the existing and proposed substations (SS) by satisfying the subjected constraints.KeywordsSubstation expansion planningDifferential evolutionTamil NaduLeast cost

Differential evolution (DE) algorithm is a heuristic approach that has a great ability to solve complex optimization and converges faster and with more certainty than many other acclaimed global optimization methods. In this paper, DE algorithm is applied to calibrate design seismic response spectra, the basic idea is that design response spectra and calculated response spectra are defined as objective function of characteristic parameters, the first turning point, the second turning point (characteristic period), the platform height and the attenuation index, of design response spectra, and the characteristic parameters are obtained by minimizing the objective function. Three pieces of the calculated response spectra for one real are calibrated as an application example. The obtained results demonstrate the effectiveness and efficiency of the proposed method.

A novel hybrid discrete differential evolution algorithm for blocking flow shop scheduling problems

Parameters setting is an important problem of evolution algorithms, include differential evolution algorithm. It has an effect on the performance of evolution algorithms. Although there is only three control parameters in differential evolution (DE) algorithm, the parameters setting is also a difficult problem. Self-adaptation is highly beneficial for adjusting the control parameters, especially when done without any user interaction. This paper presents differential evolution algorithm, called (AdaptDE), which use self-adaptive mechanisms applied to the control parameters. It can get the optimal control parameters for different optimization problem without user interaction. Experimental results indicate that AdaptDE algorithm is efficient and feasible. It is superior to jDE algorithm and FADE algorithm on the quality of solution.

Differential Evolution (DE) algorithm is a new heuristic approach which has been proposed particulary for numeric optimization problems. It is a population based algorithm like genetic algorithms using the similar operators; crossover, mutation and selection. In this work, DE algorithm has been applied to the design of fixed point digital Finite Impuls Response (FIR) filters and its performance has been compared to that of Genetic Algorithm (GA) and Least Squares Algorithm (LSQ).

Application of a Derivative-Free Method with Projection Skill to Solve an Optimization Problem

In recent years, Differential Evolution (DE) algorithm that is one of the most important delegate of the evolutionary algorithms have been successfully used for solving different optimization problems with its both serial and parallel variants. In this study, the performance of DE algorithm powered with cooperative emigrant creation strategy was investigated over the different types of numerical problems and obtained results were compared with the results of the serial DE and parallel DE algorithm supported with conventional emigrant creation strategy. Experimental studies showed that the convergence speed of the parallel DE algorithm is significantly improved with the cooperative emigrant creation strategy and the proposed parallel DE algorithm produces better results compared to the serial DE algorithm and parallel DE algorithm with the conventional emigrant creation strategy.

A self-competitive mutation strategy for Differential Evolution algorithms with applications to Proportional–Integral–Derivative controllers and Automatic Voltage Regulator systems

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.

Low-energy transfer trajectory is of growing interest in the space community. It is important to choose the patch point of the unstable manifold of the Lyapunov orbit around Sun–Earth L2 and the stable manifold of the Lyapunov orbit around Earth–Moon L2. The main contributions of this study are two areas: (a) designing the optimization model and using evolutionary algorithms to optimize the initial condition and (b) developing effective algorithms for this problem. In this article, an improved differential evolution (DE) algorithm, named adaptive uniform design differential evolution (AUDE), is proposed to solve the Earth–Moon low-energy transfer optimization problem. It incorporates the uniform design technology and the self-adaptive parameter control method into standard DE to accelerate its convergence speed and improve the stability and calculation accuracy. To verify the performance of AUDE, the Earth–Moon low-energy transfer optimization problem and 15 benchmark functions with diverse complexities are employed. The experiment results indicate that the authors' approach is able to find the better one, or at least comparably, in terms of the quality and stability of the final solutions than the other three algorithms. Moreover, it proves that the application of DE algorithm in the Earth–Moon low-energy transfer optimization problem is effective.

Aridity is a major global problem, and agricultural sectors worldwide are facing water deficiency due to various factors. This research aimed to solve the location-allocation-sizing problem (LASP) for agricultural water resources in the northeast of Thailand. The LASP model was formulated to minimize the cost of reservoir excavation and water irrigation. The differential evolution (DE) algorithm was applied to a real-world scenario involving 219 nodes of proposed water reservoirs in Ubon Ratchathani province, and its performance was compared with the genetic algorithm (GA). The results reveal that the quality of the solution from DE is significantly better than that from GA, and the results are obtained in a reasonable computational time.KeywordsLocation-Allocation-Sizing problemAgricultural water resourcesWater reservoirDifferential evolution algorithmGenetic algorithm

This paper presents a new method for optimal design of controllers based on a thought and differential evolution. At first an extensible component library and a performance criteria library are established in the approach. The component library contains different types of system components which may be essential for controller structure. Appropriate components can be selected from the library and then combined to form a controller with richer structure. The performance criteria library includes some common control performance indexes. Users can choose one from it according to performance requirements. Then, the differential evolution algorithm is utilized to optimize the parameters of each component in order to find the optimal solution and ultimately obtain the controller structure and parameters which meet requirements. Simulations were done on different types of control objects. Simulation results show that the controllers designed by the proposed method for different types of control objects generally have better comprehensive performance than the PID controllers designed by conventional methods, differential evolution and genetic algorithm. Validity, stability and generalizability of the proposed method were also tested and verified.

The differential evolution (DE) algorithm is a new heuristic approach with three main advantages: it finds the true global minimum of a multimodal search space regardless of the initial parameter values, it has fast convergence, and it uses only a few control parameters. The DE algorithm, which has been proposed particularly for numeric optimization problems, is a population-based algorithm like the genetic algorithms and uses similar operators: crossover, mutation, and selection. In this work, the DE algorithm has been applied to the design of digital finite impulse response filters, and its performance has been compared to that of the genetic algorithm and least squares method.

This parameters selection is an important issue in the research of ¿-support vector regression machine (¿-SVRM), whose nature is an optimization selection process. Motivated by the effectiveness of differential evolution (DE) algorithm on optimization problem, a new automatic searching method based on DE algorithm was proposed. Experimental results demonstrate that ¿-SVRM model optimization based on DE algorithm has better prediction capability compared with the methods based on genetic algorithm (GA), ant colony optimization (ACO) and particle swarm optimization (PSO).

Regulation-based formulas and improved Differential Evolution(DE) algorithm is used to optimize PID parameters. In order to improve the global search ability and the convergence rate of the common DE algorithm, self-adaptive method is introduced to obtain DE parameters. On the other hand, initial population quality of DE algorithm has important influence on convergence of the algorithm. So the regulation-based formulas are used to guide the production of initial population, which is good to improve the convergence rate and realize obtaining of PID parameters completely adaptive without any personal experience. The simulation is developed on steam temperature system of cycle fluidized bed boiler with serious parameter uncertainties and many disturbance and long-time delay. The results show that the improved DE algorithm has higher optimal speed, small amount of calculation and effective optimization of parameters. The proposed method has better control quality and system robustness.