Efficient Differential Evolution Algorithm Research Articles

Radial basis collocation method with parameters optimized for estimating pollutant release history.

The identification of pollutant source release history in rivers is important for emergency response of pollution accidents and formulating remediation strategies. Space-time radial basis collocation method (RBCM), as a meshless method with strong applicability, can directly estimate the release history from the concentration data measured at downstream observation sites. However, the uncertainty of specific parameters in space-time RBCM is the main factor affecting the accuracy of estimation. Therefore, a way to solve the parameters efficiently and accurately is essential. For this purpose, a new model which combines space-time RBCM and differential evolution algorithm (DEA) is established to identify the source release history. First of all, efficient parameter optimizer DEA is introduced to search the parameters that affect the estimation accuracy of space-time RBCM. Then, a new loss function considering the imbalance configuration of RBCM nodes is designed to ensure the rationality of the parameters obtained by DEA. The results of numerical cases and real field case show that the proposed method can accurately estimate the real release history with low time consumption. It is also demonstrated that DEA is more efficient than k-fold cross-validation in searching the optimal parameters for space-time RBCM, and the parameters obtained from the new loss function can make the estimated release history more precise.

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.

Pavement Condition Index Prediction Using Fractional Order GM(1,1) Model

Asphalt pavement performance prediction is an important issue for pavement management system. However, it is a difficult problem because asphalt pavement performance are affected by many factors. In this paper, a new method based on fractional gray model is proposed to predict the asphalt pavement performance with a limited data. The proposed method adopts fractional accumulating generating operation (FAGO) to replace traditional accumulating generating operation (AGO), which can be regarded as a weighted AGO emphasizing different contribution of data point for future prediction. An efficient differential evolution algorithm is adopted to select the best order of FAGO. Experimental results show that the proposed method can achieve higher prediction accuracy than conventional gray prediction model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

An efficient Differential Evolution algorithm for stochastic OPF based active–reactive power dispatch problem considering renewable generators

Optimal active–reactive power dispatch problems (OARPD) are non-convex and highly nonlinear complex optimization problems. Typically, such problems are expensive in terms of computational time and cost due to the load variations over the scheduling period. The conventional constraint-based solvers that are generally used to tackle such problems require a considerable high budget and may not provide high quality solutions. In the last decade, complexity of OARPD has further increased due to the incorporation of renewable energy sources such as: wind, solar and small-hydro generators. More specifically, the incorporation of renewable sources introduces uncertainty in generation on top of the load variations in conventional OARPD, making the problem more complicated. Recently, Differential Evolution (DE) is viewed as an excellent algorithm to solve OARPD problems, due to its effectiveness to optimize the objective function which is subject to many operational constraints. A new efficient Differential Evolution algorithm, denoted as DEa-AR, is propounded to solve the contemporary stochastic optimal power flow OARPD problems considering the renewable generators. DEa-AR uses arithmetic recombination crossover and adapts the scaling factor based on Laplace distribution. In addition, an efficient archive strategy that acts as a corresponding image of the population and stores the inferior individuals for later use, is also incorporated. The target behind using this strategy is to consider the information of inferior individuals as a direction toward finding new good solutions. The IEEE 57-bus system is used to evaluate the OARPD problems with different stochastic scenarios based on different probability distributions employed to model parameters of renewable energy sources. The performance of the proposed work is compared with other state-of-the-art algorithms. Simulation results indicate that the proposed technique can solve the OARPD problems with renewable sources effectively and can provide high quality solutions. The proposed algorithm is ranked the first with a Friedman rank equals to 1.8333 with a clear statistical significant difference compared with the most recent studies on the used problems.

A self-feedback strategy differential evolution with fitness landscape analysis

Differential evolution (DE) has been widely applied to complex global optimization problems. Different search strategies have been designed to find the optimum conditions in a fitness landscape. However, none of these strategies works well over all possible fitness landscapes. Since the fitness landscape associated with a complex global optimization problem usually consists of various local landscapes, each search strategy is efficient in a particular type of fitness landscape. A reasonable approach is to combine several search strategies and integrate their advantages to solve global optimization problems. This paper presents a new self-feedback strategy differential evolution (SFSDE) algorithm based on fitness landscape analysis of single-objective optimization problem. In the SFSDE algorithm, in the analysis of the fitness landscape features of fitness-distance correlation, a self-feedback operation is used to iteratively select and evaluate the mutation operators of the new SFSDE algorithm. Moreover, mixed strategies and self-feedback transfer are combined to design a more efficient DE algorithm and enhance the search range, convergence rate and solution accuracy. Finally, the proposed SFSDE algorithm is implemented to optimize soil water textures, and the experimental results show that the proposed SFSDE algorithm reduces the difficulty in estimating parameters, simplifies the solution process and provides a novel approach to calculate the parameters of the Van Genuchten equation. In addition, the proposed algorithm exhibits high accuracy and rapid convergence and can be widely used in the parameter estimation of such nonlinear optimization models.

Differential evolution with subpopulations for high‐dimensional seismic inversion

ABSTRACTSeismic inversion has drawn the attention of researchers due to its capability of building an accurate earth model. Such a model will need to be discretised finely, and the dimensions of the inversion problem will be very high. In this paper, we propose an efficient differential evolution algorithm and apply it to high‐dimensional seismic inversion. Our method takes into account the differences among individuals, which are disregarded in conventional differential evolution methods, resulting to a better balance between exploration and exploitation. We divide the entire population into three subpopulations and propose a novel mutation strategy with two phases. Furthermore, we optimise the crossover operator by applying the components having the best objective function values into the crossover operator. We embed this strategy into a cooperative coevolutionary differential evolution and propose a new differential evolution algorithm referred to as a differential evolution with subpopulations. Then, we apply our scheme to both synthetic and field data; the results of high‐dimensional seismic inversion have shown that the proposed differential evolution with subpopulations achieves faster convergence and a higher‐quality solution for seismic inversion.

DESRP: An efficient differential evolution algorithm for stochastic demand-oriented resource placement in heterogeneous clouds

Geographically dispersed online services receive user requests from all over the world, and the dramatic fluctuation in the user requests that arrive then introduce stochastic demands for various resources. Based on distributed cloud platforms, the application service provider must find the optimal resource placement for maximizing revenue under constraints. Nevertheless, simultaneously considering demand stochasticity and pricing heterogeneity significantly increases problem complexity. To address the problem, we propose an efficient differential evolution algorithm for stochastic demand-oriented resource placement (DESRP). Experiments using simulated and realistic data indicate that with less than triple the time cost, DESRP outperforms existing algorithms and can increase revenue by up to 27%.

Project scheduling with limited resources using an efficient differential evolution algorithm

Extensive research has been devoted to resource constrained project scheduling problem (RCPSP). Resources are renewable and there is a unique way to perform the activities. This work develops a population based evolutionary algorithm namely differential evolution (DE) to schedule project activities to minimize makespan subject to precedence constraints and resources availability. The proposed DE uses a priority value based representation to encode a project schedule and a serial generation scheme to obtain the schedule. The DE algorithm is compared with some existing algorithms available in the literature on the basis of a computational experiment performed on Patterson’s test bed. Obtained results show that the performance of the proposed DE is quite satisfactory.

An effective and efficient differential evolution algorithm for the integrated stochastic joint replenishment and delivery model

As an important managerial problem, the practical joint replenishment and delivery (JRD) model under stochastic demand can be regarded as the combination of a joint replenishment problem and traveling salesman problem, either one is an NP-hard problem. However, due to the JRD’s difficult mathematical properties, high quality solutions for the problem have eluded researchers. This paper firstly proposes an effective and efficient hybrid differential evolution algorithm (HDE) based on the differential evolution algorithm (DE) and genetic algorithm (GA) that can solve this NP-hard problem in a robust and precise way. After determining the appropriate parameters of the HDE by parameters tuning test, the effectiveness and efficiency of the HDE are verified by benchmark functions and numerical examples. We compare the HDE with the available best approach and find that the HDE can always obtain the slightly lower total costs under some situations. Compared with another popular evolutionary algorithm, results of numerical examples also show HDE is faster than GA and the convergence rate of HDE is higher than GA. HDE is a strong candidate for the JRD under stochastic demand.

An efficient differential evolution algorithm for multi-mode resource-constrained project scheduling problems

This paper considers a general resource-constrained project scheduling problem in which activities may be executed in more than one operating mode with both renewable and non-renewable resources. Each mode may have different durations and requires different amounts of renewable and non-renewable resources. To solve this NP-hard problem, an efficient differential evolution (eDE) algorithm is proposed with linear decreasing crossover factor and adaptive penalty cost. The purpose of these modifications is to enhance the ability of DE algorithm to quickly search for better solutions by encouraging solutions to escape from local solution and effectively evolve solutions in the search space. The performance of the proposed algorithm is compared with other algorithms in the literature and shows that the proposed algorithm is very efficient. It outperforms several heuristics in terms of lower average deviation from the optimal makespan. Moreover, it is capable of finding quality solutions for large-scale problems in reasonable computational time.