Self-Adaptive Differential Evolution Algorithm Applied to Water Distribution System Optimization

This paper describes the application of a novel multiobjective self-adaptive differential evolution (MOSADE) algorithm for the simultaneous optimization of component sizing and control strategy in parallel hybrid electric vehicles (HEVs). Based on an electric assist control strategy, the HEV optimal design problem is formulated as a nonlinear constrained multiobjective problem with competing and noncommensurable objectives of fuel consumption and emissions. The driving performance requirements are considered constraints. The proposed MOSADE approach adopts an external elitist archive to retain nondominated solutions that are found during the evolutionary process. To preserve the diversity of Pareto optimal solutions, a progressive comparison truncation operator based on the normalized nearest neighbor distance is proposed. Moreover, a fuzzy set theory is employed to extract the best compromise solution. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the file transfer protocol; 2) ECE+EUDC; and 3) Urban Dynamometer Driving Schedule. The results demonstrate the capability of the proposed approach to generate well-distributed Pareto optimal solutions of the HEV multiobjective optimization design problem. The comparison with the reported results of genetic-algorithm-based weighting sum approaches and Nondominated Sorting Genetic Algorithm II reveals the superiority of the proposed approach and confirms its potential for optimal HEV design.

Multi-objective optimization of p -xylene oxidation process using an improved self-adaptive differential evolution algorithm

Differential Evolution (DE) is possibly the most current powerful stochastic real-parameter optimization algorithm and has been used in multiple diverse area such as neural networks, logistics, scheduling, modelling and others. Its simplicity, ease of implementation and reliability had captures many practitioners and scientists in implementing the algorithm. As different problems require different parameter setting, the implementation of DE in tackling complex computational optimization problem is quite challenging. Nevertheless, success of the algorithm depends on the ability to choose the right parameter setting based on problems in hand. Thus, extra attention is needed in order to fine tune the perfect parameter for each problem. Self-adaptive Differential Evolution (SADE) algorithm had been introduced in order to simplify the search for the right parameter to be used in DE algorithm. With the introduction of SADE in optimization areas, where the choice of learning strategy and parameter setting do not require predefining, parameter tuning has become less confusing. This paper aims at providing an overview on significant application that have benefited from SADE implementation. SADE had been applied in numerous disciplines such as electromagnetics, power system, computer performance, fermentation, polyester process and more. SADE has also proven to achieve better performance compared to conventional DE algorithm. By collecting and analyzing related articles that have implemented SADE in solving problem, a significant trends on the application of SADE will be provided.

Differential evolution (DE) is an efficient population-based search algorithm for solving numerical optimization problems. However, the performance of DE is very sensitive to the choice of mutation strategies and their associated control parameters. In this paper, we propose a self-adaptive multi-population differential evolution algorithm, called SAMDE. The population is randomly divided into three equally sized sub-populations, each with different mutation strategies. At the end of each generation, all sub-populations are updated independently and recombined. Each sub-population uses an adaptive mechanism for selecting how current generation control parameters are generated. An improved mutation strategy, “rand assemble/1”, is proposed, its base vector is composed proportionally of three randomly selected individuals. The performance of SAMDE is evaluated on the suite of CEC 2005 benchmark functions. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that SAMDE has a competitive performance compared to several other efficient DE variants.

A novel compact ultra-broadband-modified fork-shaped printed antenna is optimized efficiently by using a self-adaptive hybrid differential evolution (SHDE) algorithm. Firstly, a novel compact ultra-broadband-modified fork-shaped printed antenna structure is proposed. The antenna with more compact size consists of a modified fork-shaped radiator and a modified ground plane, which can cover a very wide operating frequency band. The antenna is fed by a step-shaped microstrip line, and the modified ground plane consists of some rectangular slots and an L-shaped stub. Then, the SHDE algorithm is used to determine structural dimensions of the proposed antenna, and the antenna’s performance is optimized while maintaining a cost-effective computation time. The optimized antenna with only 11.8 mm × 19.7 mm size covers -10 dB reflection coefficient bandwidth of 147.6% from 3.08 to 20.46 GHz. Finally, the antenna prototype is fabricated, and the measured results basically agree with the simulated ones. The proposed antenna can be viewed as an excellent candidate for realizing ultrabroadband transmission technology.

This paper studied a flowshop scheduling problem with fuzzy due dates where the objective of maximizing the sum of customer satisfaction for the completion times of jobs is concerned. A self-adaptive differential evolution (SDE) algorithm has been employed to solve the fuzzy flowshop problem (FFP). The mutation scaling factor F and crossover probability CR are self-adaptive during the evolutionary process which can balance the exploration and the exploitation effectively. In order to make the SDE algorithm more effective and efficient, a local search strategy based on insert operation is introduced to exploit more latitude of search space to anchor the global optimum. The experimental results on the problem instance from literature demonstrate the good performance of the proposed SDE, thereby proving its worth as an attractive alternative for flowshop problems in uncertain environments.

Differential Evolution (DE) algorithm is powerful in optimization problems over several real parameters. DE depends on strategies to generate new trial solutions and the associated parameter values for searching performance. In self-adaptive DE, the automatic learning about previous evolution was used to determine the best mutation strategy and its parameter settings. By combining the self-adaptive DE and Hooke Jeeves local search, we developed a new docking method named SADock (Strategy Adaptation Dock) with the help of AutoDock4 scoring function. As the accuracy and performance of SADock was evaluated in self-docking using the Astex diverse set, the introduced SADock showed better success ratio (89%) than the success ratio (60%) of the Lamarckian genetic algorithm (LGA) of AutoDock4. The self-adapting scheme enabled our new docking method to converge fast and to be robust through the various docking problems.

Differential Evolution (DE) has been shown to be a powerful evolutionary algorithm for global optimization in many real problems. Self-adaptation has been found to be high beneficial for adjusting control parameters during evolutionary process, especially when done without any user interaction. In this paper we investigate a self-adaptive differential evolution algorithm where more DE strategies are used and control parameters F and CR are self-adapted. The performance of the self-adaptive differential evolution algorithm is evaluated on the set of 24 benchmark functions provided for the CEC2006 special session on constrained real parameter optimization.

A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

Variety of real-world optimization problems can be successfully solved by employing a powerful technique, called Differential Evolution (DE) algorithm. The popularity of DE has grown tremendously since its inception as it includes a very few number of control parameters. However, the selection or tuning of these parameters plays a crucial role in determining the performance of the algorithm in terms of its convergence behaviour. In this paper, a novel Self-Adaptive DE (SADE) approach has been proposed for the de sign of a multiplier-less low-pass linear-phase FIR filter to improve the computational efficiency of the algorithm. For this purpose, the convergence behaviour of the SADE technique has been presented and it has been compared with that of traditional DE technique. Additionally, the performance of the SADE-optimized filter has been evaluated in terms of its magnitude response. The corresponding magnitude response for the DE-optimized filter has also been presented for comparison. It has been established that the proposed SADE algorithm outperforms the traditional DEfor this particular design problem.

Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.

An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system

Railway projects frequently pass through ecologically fragile regions and result in various ecological damages. The railway alignment determining the layout of structures plays an important role in ecological protection. This paper presents a railway alignment optimization model based on multiobjective bi-level programming (MBRAO). The upper level of MBRAO is a horizontal alignment optimization for the minimization of investment and ecological impacts from tunnel drainage and train noise. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) solves the horizontal alignment optimization and naturally maintains population diversity. The lower level of MBRAO is a vertical alignment optimization for investment minimization. A self-adaptive differential evolution (DE) algorithm adjusting essential parameters based on optimization status solves the vertical alignment optimization efficiently. MBRAO is applied in multistage at both levels to find the suitable numbers of horizontal and vertical points of intersection. This paper introduces three kinds of vertical feature data generated corresponding to horizontal feature data to define different site modification costs on the bridge, subgrade, and tunnel structures. A real-world case study at Wolong Reserve is studied to verify the effectiveness of MBRAO based on a customized geographic information system (GIS).

Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm

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.