Search

Electricity theft has emerged as one of the major reasons of Non-Technical Losses (NTLs) in the power distribution systems and has become a global issue. Therefore, power utilities are concerned about resolving the issue of electricity theft. In this regard, the data collected by Advanced Metering Infrastructure (AMI) can be used to devise data-driven machine learning-based Electricity Theft Detection (ETD) methods. In this paper, a novel data-driven ETD method is proposed that firstly labels the electricity consumers as fair or malicious based on three analyses: intra-consumer, inter-consumer, and temperature-electricity consumption relation. After assigning labels to the data, significant features are extraction from data by introducing a new feature extractor that is based on Reconstruction Independent Component Analysis (RICA) and sparse auto-encoder. Finally, classification is performed using two newly proposed enhanced classifiers, named as Differential Evolution (DE) Random Undersampling Boosting (DE-RUSBoost) and Jaya-RUSBoost. The performance of RUSBoost is enhanced using two nature-inspired swarm intelligence-based optimization algorithms, namely DE and Jaya optimization. The performance evaluation of the proposed classifiers is performed by conducting comprehensive simulations on real-world data taken from the UMass* smart homes electricity consumption dataset and the State Grid Corporation of China (SGCC) electricity theft dataset. DE-RUSBoost achieves an Area Under the Curve (AUC) of 0.89 and Jaya-RUSBoost achieves AUC of 0.95. The proposed classifiers have superior performance compared to two state-of-the-art benchmarks, i.e., Wide And Deep Convolution Neural Network (WADCNN) and grid search-based RUSBoost.

In this article, the concurrent optimal design of a planar five-bar parallel robot for a high-speed pick and place task is considered. A trade-off between trajectory tracking, energy consumption and deformation of the flexible links is sought. Due to the high-speed operation, the minimization of the vibratory effect is considered since the design stage. Thus, the design is stated as a non-linear multi-objective dynamic optimization problem that is solved through the Differential Evolution (DE) algorithm, as well as, feasibility rules for constraints handling. Mechanical structural variables that modify the inertial parameters of the rigid and flexible links, as well as, control variables related to gains of a PID controller are considered as independent variables. This problem is subject to maximum torque that each motor could provide; inherent constraints for link manufacture; dimensional synthesis; Grashof’s criterion and initial and boundary conditions. Results show that it is possible, despite the vibrational phenomenon, to reduce the energy consumption without loss of precision through an appropriate mass balance of the actuated links.

Due to the computational complexity of multilevel image thresholding, Swarm Intelligence Optimization Algorithm (SIOA) has been widely applied to improve the calculation efficiency. Therefore, more and more attention has been paid to exploring the application of the latest SIOA in multilevel segmentation. This article takes Otsu and fuzzy entropy as the objective functions, using Coyote Optimization Algorithm (COA) for multilevel thresholds optimization selection, through fuzzy median aggregation of local neighborhood information and then forms the Fuzzy Coyote Optimization Algorithm (FCOA), so that the thresholding image segmentation can be achieved in the end. To prevent the COA algorithm from falling into the local optimum, this article follows the differential evolution strategy adopted by the standard COA, using the number of iterations to construct the differential scaling factor to form the Improved Coyote Optimization Algorithm (ICOA). The experimental results show that fuzzy Kapur entropy and fuzzy median value aggregation-based ICOA(FICOA) achieves better image segmentation quality. Compared with Grey Wolf Optimizer (GWO), Fuzzy Modified Quick Artificial Bee Colony and Aggregation Algorithm (FMQABCA) and Fuzzy Modified Discrete Grey Wolf Optimizer and Aggregation Algorithm (FMDGWOA), FCOA and FICOA have certain advantages in visual effects of image segmentation and PSNR, FSIM evaluation indices. Particularly compared with GWO (also a wolf evolutionary algorithm), FICOA shows significant advantages.

An optimized design with real-time and multiple realistic constraints in complex engineering systems is a crucial challenge for designers. In the non-uniform Internet of Things (IoT) node deployments, the approximation accuracy is directly affected by the parameters like node density and coverage. We propose a novel enhanced differential crossover quantum particle swarm optimization algorithm for solving nonlinear numerical problems. The algorithm is based on hybrid optimization using quantum PSO. Differential evolution operator is used to circumvent group moves in small ranges and falling into the local optima and improves global searchability. The cross operator is employed to promote information interchange among individuals in a group, and exceptional genes can be continued moderately, accompanying the evolutionary process’s continuance and adding proactive and reactive features. The proposed algorithm’s performance is verified as well as compared with the other algorithms through 30 classic benchmark functions in IEEE CEC2017, with a basic PSO algorithm and improved versions. The results show the smaller values of fitness function and computational efficiency for the benchmark functions of IEEE CEC2019. The proposed algorithm outperforms the existing optimization algorithms and different PSO versions, and has a high precision and faster convergence speed. The average location error is substantially reduced for the smart parking IoT application.

Chaotic maps used to shuffle and manipulate the image pixels are important for image encryption (IE). In this study, a novel 2D optimized chaotic map (OPMAP) using a triple-objective differential evolution (TODE) algorithm is presented for IE. A model for OPMAP with eight decision variables is empirically designed, and then its variables are determined utilizing TODE through minimizing a triple-objective function that involving Lyapunov exponent (LE), entropy and 0–1 test. OPMAP is assessed with respect to credible measurements like bifurcation, 3D phase space, LE, 0–1 test, permutation entropy (PE) and sample entropy (SE). The capability of OPMAP is then verified through an IE scheme including permutation and diffusion through various cryptanalyses: key space 2298, mean entropy 7.9995, mean correlation 13.61E-5, number of pixels changing rate (NPCR) 99.6093, unified average changing intensity (UACI) 33.4630 and encryption processing time (EPT) 0.2919 (s). A detailed review of IE schemes reported elsewhere is presented and IE performance of OPMAP is also validated by comparison with those IE schemes with and without optimization used. The 2D-OPMAP-based IE is faster and has low computational complexity. Moreover, the proposed it shows better cryptanalysis results for the most of the comparisons.

The fusion of bio-inspired algorithms into online controller tuning (adaptive controller tuning) is one of the main topics in Intelligent Control. One crucial issue is to reduce the times that the tuning process performs over time. In this work, a novel Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the number of tuning process activations, and hence, this promotes resource savings in the overall computational cost for the tuning process. In this approach, an event function is designed to determine the control parameter update where the use of identification and predictive stages set the current control parameters. Furthermore, an elitist initialization in the differential evolution algorithm is also incorporated for solving the optimization problems at each stage. The speed regulation of the DC motor under disturbances is the study case in the ACCT approach. Comparative results with state-of-the-art bio-inspired algorithms in control tuning reveal in the AACT approach that the elitist initialization in differential evolution notably benefits the controller performance. Moreover, the comparative results with the Synchronous Adaptive Controller Tuning (SACT) approach show that the proposal reduces 61% the tuning process computation frequency with a similar speed regulation performance when disturbances appear.

The great advancements in communication, cloud computing, and the internet of things (IoT) have opened critical challenges in security. With these developments, cyberattacks are also rapidly growing since the current security mechanisms do not provide efficient solutions. Recently, various artificial intelligence (AI) based solutions have been proposed for different security applications, including intrusion detection. In this paper, we propose an efficient AI-based mechanism for intrusion detection systems (IDS) in IoT systems. We leverage the advancements of deep learnings and metaheuristics (MH) algorithms that approved their efficiency in solving complex engineering problems. We propose a feature extraction method using the convolutional neural networks (CNNs) to extract relevant features. Also, we develop a new feature selection method using a new variant of the transient search optimization (TSO) algorithm, called TSODE, using the operators of differential evolution (DE) algorithm. The proposed TSODE uses the DE to improve the process of balancing between exploitation and exploration phases. Furthermore, we use three public datasets, KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 to assess the performance of the developed method, which achieved higher accuracy compared to several existing approaches.

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching Algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.

Seepage monitoring is a vital task in the risk management of concrete dams. Considering the lag effect of input factors, this paper presents a novel seepage monitoring model for concrete dams and proposes an effective identification method of lag process. Firstly, extreme gradient boosting (XGBoost) were adopted to predict the dam seepage. Hybridizing grey wolf optimization (HGWO) which integrates differential evolution (DE) into grey wolf optimization (GWO) and five–fold cross validation were utilized to optimize the hyper–parameters of XGBoost. Secondly, under the same search range and four evaluation indicators, the models optimized respectively by HGWO and three other algorithms were compared to confirm the global optimization capability of HGWO. Six state–of–art methods were also introduced to verify the effectiveness and feasibility of the proposed model. Then, based on the computation method of factor importance in decision tree models, we evaluated the relative importance of each component in the proposed model. Finally, according to the factor importance, the lag process of upstream water level and rainfall was identified, meanwhile a new equivalent water level calculation method is proposed. Monitoring data from three piezometric tubes on a concrete dam were taken as the experimental object. The results show that the improved HGWO has stronger global optimization ability, and the HGWO–XGBoost model achieves satisfactory prediction for seepage in concrete dams. Compared with the traditional trial–and–error method, the lag process computation method proposed in this paper provides a better recognition effect, which is of great value to the seepage monitoring and control of concrete dams.

A fully automated design is proposed in this work employing optimal deep learning features for classifying gastrointestinal infections. Here, three prominent infections– ulcer, bleeding, polyp and a healthy class are considered as class labels. In the initial stage, the contrast is improved by fusing bi-directional histogram equalization with top-hat filtering output. The resultant fusion images are then passed to ResNet101 pre-trained model and trained once again using deep transfer learning. However, there are challenges involved in extracting deep learning features including impertinent information and redundancy. To mitigate this problem, we took advantage of two metaheuristic algorithms– Enhanced Crow Search and Differential Evolution. These algorithms are implemented in parallel to obtain optimal feature vectors. Following this, a maximum correlation-based fusion approach is applied to fuse optimal vectors from the previous step to obtain an enhanced vector. This final vector is given as input to Extreme Learning Machine (ELM) classifier for final classification. The proposed method is evaluated on a combined database. It accomplished an accuracy of 99.46%, which shows significant improvement over preceding techniques and other neural network architectures.

In this paper, a Self-learning Collective Intelligence Differential Evolution (SLCIDE) algorithm was proposed to optimize both the architecture and parameters of a Feedforward Neural Network (FNN). In order to improve the exploration-exploitation capability, a new Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-learning mechanism which was designed to adaptively select m top ranked donor vectors was developed by using a widely used unsupervised learning method, k-means. As a result, the proposed approach can be more adaptive and statistically powerful on versatile problems. Then, we evaluated the performances of the proposed SLCIDE approach by studying some numerical optimization problems of CEC 2013 with D = 30 and D = 50. Further, the proposed SLCIDE method was applied to train a FNN on four most popular datasets, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SLCIDE method obtain better results compared with other state-of-the-art algorithms.

With the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in a ship microgrid. In order to balance the two non-compatible objectives, a new differential evolution variant, which is named as SaCIDE-r, was proposed to solve the optimization problem. In this algorithm, a Collective Intelligence (CI) based mutation operator was proposed by mixing some promising donor vectors in the current population. Besides, a self-adaptive mechanism which was developed to avoid introducing extra control parameters. Further, to avoid being trapped in local optima, a re-initialization mechanism was developed. Then, we have evaluated the performances of the proposed SaCIDE-r approach by studying some numerical optimization problems of Congress on Evolutionary Computation (CEC) 2013 with D = 30, compared with seven stateof-the-art DE algorithms. Moreover, the proposed SaCIDE-r method was applied for economic scheduling of a shipboard microgrid under different cases compared with other multi-objective optimizing methods, resulting in very competitive performances. The comprehensive experimental results have demonstrated that the presented SaCIDE-r method might be a feasible solution for such a kind of optimization problem.

In today's age of industrialization, sensor devices installed on equipment generate a vast amount of data. One of the engineers' main jobs is utilizing these data to provide better solutions to industrial problems. This availability of extensive data partly led to the creation of predictive maintenance (PdM). In PdM, existing and previous conditions of devices are used to predict their future behavior for optimal maintenance. Most of these PdM approaches are typical time-series predictions. Machine learning tools like Recurrent Neural Networks (RNNs) are excellent tools for time-series predictions. However, most RNNs suffer from training issues due to the unstable gradient problem. Thus, networks such as the Echo State Network (ESN), were designed to solve them. The ESN solves the gradient problem by training only the output weights using simple linear regression. Despite this ease, the selection of ESN parameters and topology is a considerable design challenge. This problem is often formulated as a typical optimization problem. Metaheuristic algorithms are known to be excellent tools for solving optimization problems. Hence, in this work, we design an improved Grasshopper Optimization Algorithm (GOA) based ESN. The proposed technique uses a new solution representation with a simplified attraction and repulsion mechanisms to enhance performance. Our target application is to predict the Remaining Useful Life (RUL) of turbofan engines. The method outperforms the Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Binary PSO (BPSO), the original GOA, the classical ESN, deep ESN, and LSTM. We have provided all implemented codes and data at the GitHub repository. https://github.com/bala-221/Airplane-fault-prediction.

The state of charge (SOC) estimation of Li-ion batteries has attracted substantial interests in recent years. Kalman Filter has been widely used in real-time battery SOC estimation, however, to build a suitable dynamic battery state-space model is a key challenge, and most existing methods still use the off-line modelling approach. This paper tackles the challenge by proposing a novel sparse learning machine for real-time SOC estimation. This is achieved first by developing a new learning machine based on the traditional least squares support vector machine (LS-SVM) to capture the process dynamics of Li-ion batteries in real-time. The least squares support vector machine is the least squares version of the conventional support vector machines (SVMs) which suffers from low model sparseness. The proposed learning machine reduces the dimension of the projected high dimensional feature space with no loss of input information, leading to improved model sparsity and accuracy. To accelerate computation, mapping functions in the high feature space are selected using a fast recursive method. To further improve the model accuracy, a weighted regularization scheme and the differential evolution (DE) method are used to optimize the parameters. Then, an unscented Kalman filter (UKF) is used for real-time SOC estimation based on the proposed sparse learning machine model. Experimental results on the Federal Urban Drive Schedule (FUDS) test data reveal that the performance of the proposed algorithm is significantly enhanced, where the maximum absolute error is only one sixth of that obtained by the conventional LS-SVMs and the mean square error of the SOC estimations reaches to 10-7, while the proposed method is executed nearly 10 times faster than the conventional LS-SVMs.

Recently, fuel cells (FCs) have found vast employment in several applications. However, unique maximum power point tracking (MPPT) exists for each set of operating condition for the efficient operation of FCs. Therefore, this paper presents a differential evolution optimization algorithm (DEOA)-based optimized fuzzy-logic (OFLC) MPPT method for enhancing the maximum power extraction of FCs. The various settings for the membership functions (MFs) of the input and output variables are optimized in the proposed method. Thence, more degree-of-freedom can be employed for accurate and fast tracking of the optimal power point of the proton exchange membrane FCs (PEMFCs). Whereas, existing MPPT methods in the literature for FC applications suffer from decreased degree-of-freedom for optimizing their performance, and lack of adaptivity, which obstructs their suitability for the wide operating range of FCs. The superiority and performance effectiveness of the proposed OFLC MPPT method have been validated and compared with the most prevalent techniques in the literature. Moreover, the robustness and sensitivity of the proposed OFLC MPPT method have been tested at various step changes in the water content of membrane and various temperature changes. Moreover, the proposed design of the suggested OFLC MPPT is general and it can be implemented on low-cost microcontrollers. The results verify the superior performance of the proposed OFLC MPPT method from the accurate and fast MPPT extraction, smooth output power with low ripple, and simplicity of the design point of views.

Obtaining suitable parameters of photovoltaic models based on measured current-voltage data of the PV system is vital for assessing, controlling, and optimizing photovoltaic systems. To acquire specific parameters of photovoltaic models, we proposed a meta-heuristic algorithm named hybrid symbiotic differential evolution moth-flame optimization (HSDE-MFO) algorithm. The proposed algorithm implements our new proposed symbiotic algorithm structure (SAS). This structure is inspired by soybean-rhizobium nodule symbiosis in nature. The proposed SAS divides the population into two parallel working sub-groups, i.e., soybean group and rhizobium group. Soybean group that focuses on exploration is updated by the strategies in the DE algorithm; the rhizobium group that emphasizes on exploitation is renewed by the strategies in the MFO algorithm. Artificial particle selection strategy and artificial flames generation strategy are developed to generate high-quality mutant materials and high-quality flames, respectively. The above-proposed methods balance the exploration ability and exploitation ability and ensure a bionic structure of the proposed algorithm. Moreover, a new elite strategy is developed to offer a chaotic particle to further refine the quality of the current population. The proposed HSDE-MFO is employed to solve the parameters identification problem of photovoltaic models, i.e., single diode, double diode, and photovoltaic module and compared with recently well-established algorithms. Experimental results indicate that HSDE-MFO can acquire precise parameters of the three photovoltaic models and stable performance in 30 independent runs.

Scheduling of community microgrids (CMGs) is an important and challenging optimization problem. Generally, the optimization is performed to schedule resources of CMGs at minimum cost. In recent years, a number of algorithms have been proposed to solve such problems. However, the performance of these algorithms is far from ideal due to the presence of different complex equality and inequality constraints in CMGs. Furthermore, most of the current works ignore energy storage (ES) degradation costs in the optimization model, which has a significant impact on the life of ES. This paper develops both single and bi-objective optimization models by considering the life of ES along with the operating cost for scheduling a CMG. An efficient heuristic-enhanced Differential Evolution (DE) approach is proposed to solve these models; by exploiting the structure of equality constraints, the proposed heuristic is able to generate feasible solutions quickly. The significance of the proposed heuristic is that it can generate a high-quality solution with a considerably lower computational effort. Numerical simulations were performed to evaluate the performance of the proposed method, and obtained results were compared with the state-of-the-art algorithm. The simulation results corroborate the efficacy of the proposed method.

For the purpose of addressing the multi-objective optimal reactive power dispatch (MORPD) problem, a two-step approach is proposed in this paper. First of all, to ensure the economy and security of the power system, the MORPD model aiming to minimize active power loss and voltage deviation is formulated. And then the two-step approach integrating decision-making into optimization is proposed to solve the model. Specifically speaking, the first step aims to seek the Pareto optimal solutions (POSs) with good distribution by using a multi-objective optimization (MOO) algorithm named classification and Pareto domination based multi-objective evolutionary algorithm (CPSMOEA). Furthermore, the reference Pareto-optimal front is generated to validate the Pareto front obtained using CPSMOEA; in the second step, integrated decision-making by combining fuzzy c-means algorithm (FCM) with grey relation projection method (GRP) aims to extract the best compromise solutions which reflect the preferences of decision-makers from the POSs. Based on the test results on the IEEE 30-bus and IEEE 118-bus test systems, it is demonstrated that the proposed approach not only manages to address the MORPD issue but also outperforms other commonly-used MOO algorithms including multi-objective particle swarm optimization (MOPSO), preference-inspired coevolutionary algorithm (PICEAg) and the third evolution step of generalized differential evolution (GDE3)., Comment: Accepted by IEEE Access

Human hands engage in interactive activities in many practical working scenarios, among which the interactions between human hands and objects are the most common. Tracking the movement of the human hand during hand-object interactions is an important research task that is also challenging due to the high-dimensionality and occlusions. In this paper, we track hand-object interactions from depth observations with a model-based method. To overcome the difficulties of optimum searching in the hand-object high-dimensional space, we propose a new algorithm - collaborative differential evolution filtering (CoDEF) - for tracking hand-object interactions. The proposed CoDEF algorithm integrates the differential evolution (DE) algorithm into a particle filtering (PF) framework to accelerate the convergence of particles. Particles are driven to the regions with a high probability by optimizing the matching error under the current observation with DE. To decompose the state space and decrease the complexity of optimum searching, CoDEF tracks the movement of the hand and object by using two collaborative trackers. Based on the proposed CoDEF algorithm, we develop a model-based tracking system with 3D graphic techniques. According to the experimental results, the proposed CoDEF algorithm can achieve robust tracking of hand-object interactions using fewer particles.

Designing fitness-based adaptive scaling factor (F) is an effective method to enhance the performance of differential evolution (DE) algorithms. This paper investigates the correlations between F and fitness values of target vectors, base vectors and difference vectors. The correlations are described by the notations of monotonicity and nonlinearity. Monotonicity is used to examine whether the optimization performance of DE and the fitness values of certain vectors have positive or negative correlation. Nonlinearity denotes the operation in which nonlinear mappings are used to redistribute the values of F in [0, 1] so as to boost the optimization performance. These two aspects of correlations are empirically tested on the Numerical Optimization Competition benchmark functions in IEEE Congress on Evolutionary Computation. Simulation results reveal different qualitative and quantitative correlations between F and fitness values of different vectors. Then, a new F that combines these relations is designed. Its strength is numerically verified by testing different CEC Benchmark functions.

High-performance microelectromechanical systems (MEMS) are playing a critical role in modern engineering systems. Due to computationally expensive numerical analysis and stringent design specifications nowadays, both the optimization efficiency and quality of design solutions become challenges for available MEMS shape optimization methods. In this paper, a new method, called self-adaptive surrogate model-assisted differential evolution for MEMS optimization (ASDEMO), is presented to address these challenges. The main innovation of ASDEMO is a hybrid differential evolution mutation strategy combination and its self-adaptive adoption mechanism, which are proposed for online surrogate model-assisted MEMS optimization. The performance of ASDEMO is demonstrated by a high-performance electro-thermo-elastic micro-actuator, a high-performance corrugated membrane micro-actuator, and a highly multimodal mathematical benchmark problem. Comparisons with state-of-the-art methods verify the advantages of ASDEMO in terms of efficiency and optimization ability.

As fossil fuel is being depleted, the percentage of wind power capacity in total electricity generation is increasing. In order to improve the absorption capacity of wind power, wind power prediction has been introduced. Aiming at the disadvantage of low prediction accuracy and unstable model of traditional extreme learning machine (ELM), a kernel extreme learning machine based on differential evolution (DE) and cross validation optimization method is proposed to predict short-term wind power generation. Firstly, the average mean square error (MSE) verified by k folding and cross validation is adopted as the error function of the model to improve the stability and generalization performance of the model. Secondly, differential evolution algorithm is used to optimize the regularization coefficient and kernel width of the kernel extreme learning machine with cross validation and improve the precision of model is 8.34%. Finally, compared with the application of extreme learning machine with genetic algorithm and cross validation to a wind farm prediction case in northwest China, the experimental results show that the convergence rate of this method is twice that of genetic algorithm (GA) optimization algorithm, and the accuracy is higher.

This paper discusses the performance of a multi-method ensemble meta-heuristic for optimization in a problem of submerged arches design. Specifically, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL) is proposed. It is a multi-method evolutionary ensemble which combines different searching structures in a single population. In this case, some novel searching strategies such as the Firefly algorithm and the Water Wave Optimization have been proposed to be included in the CRO-SL algorithm. In addition, some other traditional searching structures such as classical crossovers, Gaussian mutation operators, Harmony search and Differential Evolution search strategies have also been implemented in the algorithm. The proposed CRO-SL has been applied to the optimal design of submerged arches in deep waters, considering several design aspects such as the bending moment of the beam and the airspace of the arch. The evaluation of the submerged arches quality is obtained after the simulation of the arches by means of a finite elements computational software, which has been hybridized with the CRO-SL algorithm in the process of fitness calculation. It will be shown that the CRO-SL is able to obtain excellent solutions to submerged arches design problems. For this, the computational performance of each substrate (different searching strategies) in this optimization problem will be discussed and the results obtained will be compared with those in alternative works for similar experiments.

The precision of part machining is influenced by the tool life. Tools gradually wear out during the cutting process, which reduces the machining accuracy. Many studies have used machining parameters and sensor signals to predict flank wear; however, these methods have many limitations related to sensor installation, which is not only time-consuming and costly but also impractical in industry. This paper proposes an interval type-2 fuzzy neural network (IT2FNN) based on the dynamic-group cooperative differential evolution algorithm for flank wear prediction. Moreover, the Taguchi method is used to design cutting experiments for collecting experimental data and reducing the number of experiments. The CIE-xy color chromaticity values, spindle speed, feed per tooth, cutting depth, and cutting time are used as inputs of the IT2FNN, and the output is the flank wear value. The experimental results indicate that the proposed method can effectively predict flank wear with higher efficiency than other algorithms.

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.

In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FA-DE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm’s performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

Differential Evolution (DE) is a simple and effective stochastic algorithm for optimization problems, and it became much more popular in recent year because of its easy-implementation and excellent performance. Nevertheless, the performance of DE algorithm is greatly affected by the trial vector generation strategy including mutation strategy and parameter control, both of which still exist some weaknesses, e.g. the premature convergence to some local optima of a mutation strategy and the misleading interaction among control parameters. Therefore in this paper, a novel Di-DE algorithm is proposed to tackle these weaknesses. A depth information based external archive was advanced in our novel mutation strategy, which can get a better perception of the landscape of objective in an optimization. Moreover, a novel grouping strategy was also employed in Di-DE and parameters were updated separately so as to avoid the misleading among parameters. Moreover, a cooperative strategy for information interchange was also advanced aiming at improving the efficiency of the exploration behavior. By absorbing these advancements, the novel Di-DE algorithm can secure better performance in comparison with other famous optimization algorithms. The algorithm validation was conducted on CEC2013 and CEC2017 test suites, and the results revealed the competitiveness of our Di-DE algorithm in comparison with those famous optimization algorithms including Particle Swarm Optimization (PSO) variants, QUasi-Affine TRansformation Evolution (QUATRE) variants and Differential Evolution (DE) variants.

Mobile robots have recently attracted the attention and applicability in field areas ubiquitously. Within the context of autonomous navigation, path planning is relevant for comfortability, safety, execution time and energy savings. In this paper, we propose an approach to suggest smooth paths from observed robot trajectories by optimizing fitting and smoothness criteria using Differential Evolution with distinct modes of initialization, selection pressure, exploration and exploitation. Our rigorous computational experiments using a relevant set of real-world robot trajectories from the Boe-Bot mobile robot architecture show the feasibility and efficiency of our approach in computing smooth curves, suggesting the superior performance of the greedy initialization scheme based on the triangular convex hull of the robot trajectory, and Differential Evolution with exploitative and parameter adaptation schemes such as Rank-Based Differential Evolution (RBDE), Adaptive Differential Evolution with External Archive (JADE) and Strategy Adaptation Differential Evolution (SADE). Our obtained results offer the building blocks to further advance towards developing data-driven curve fitting and path planning algorithms, which may find use in several real-world applications in Robotics and Operations Research.

Among many evolutionary algorithms, differential evolution (DE) has received much attention over the last two decades. DE is a simple yet powerful evolutionary algorithm that has been used successfully to optimize various real-world problems. Since it was introduced, many researchers have developed new methods for DE, and one of them makes use of a mutation based on the Cauchy distribution to increase the convergence speed of DE. The method monitors the results of each individual in the selection operator and performs the Cauchy mutation on consecutively failed individuals, which generates mutant vectors by perturbing the best individual with the Cauchy distribution. Therefore, the method can locate the consecutively failed individuals to new positions close to the best individual. Although this approach is interesting, it fails to take into account establishing a balance between exploration and exploitation. In this paper, we propose a sigmoid based parameter control that alters the failure threshold for performing the Cauchy mutation in a time-varying schedule, which can establish a good ratio between exploration and exploitation. Experiments and comparisons have been done with six conventional and six advanced DE variants on a set of 30 benchmark problems, which indicate that the DE variants assisted by the proposed algorithm are highly competitive, especially for multimodal functions.

We propose a two-stage niching algorithm that separates local optima areas in the first stage and finds the optimum point of each area using any optimization technique in the second stage. The proposed first stage has complementary characteristics to the shortcoming of Nearest-better Clustering (NBC). We introduce a weighted gradient and distance-based clustering method (WGraD) and two methods for determining its weights to find out niches and overcome NBC. The WGraD creates spanning trees by connecting each search point to other suitable one decided by weighted gradient information and weighted distance information among search points. Since weights influence its clustering result, we propose two weight determination methods 1 and 2. The weight determination method 1 firstly forms one spanning tree and then uses a dynamic pruning method and the Hill-Valley test to cut long edges and repair them. The weight determination method 2 assigns different weights to different search points based on distance information. We combine these methods into WGrad, i.e. WGraD 1 and WGraD 2 , and compare the characteristics of NBC, WGraD 1 , and WGraD 2 using differential evolution (DE) as a baseline search algorithm for obtaining the optimum of each niche after clustering local areas. We design a controlled experiment and run (NBC + DE), (WGraD 1 + DE) and (WGraD 2 + DE) on 8 benchmark functions from CEC 2015 test suite for single objective multiniche optimization. The experimental results confirmed that the proposed strategy can overcome the shortcoming of NBC and be a complementary niche method of NBC.

This paper proposes a novel differential evolution operator to enhance the rational exploration of the multi-objective optimization based on decomposition for solving the extended environmental/economic dispatch problem. In practical power engineering, besides the fuel cost and harmful emission become the major concerns, transmission loss does as well simultaneously. Meanwhile, the increasing near-real-time decisions push the conventional differential evolution as a solver to promote the solving speed. Therefore, a dynamic response surface fitting between the control vector and the fitness value is introduced for getting the approximation relationship that can provide a sequence of rational searching directions to accelerate the solving process to approach the optima of the 3-objective optimization. The proposed approach shows promising performance while simulated by using DTLZ1-4 and IEEE-30 standard power systems.

Low switching frequency modulation techniques such as Selective Harmonic Elimination - Pulse Width Modulation (SHE-PWM) or Optimal Pulse Pattern (OPP) are commonly used in medium voltage - high power multilevel converters to improve their efficiency. These techniques require solving non-linear equations in order to calculate the firing angles. These equations can be solved by meta-heuristic optimization algorithms, which depend on a set of hyperparameters that must be properly adjusted to ensure their effectiveness. This paper presents a methodology to tune different meta-heuristic algorithms to fairly compare them and select the best algorithm to solve SHE-PWM/OPP formulation. The methodology is applied considering different meta-heuristic algorithms such as genetic algorithm, differential evolution, harmony search and simulated annealing. It has been validated in SHE-PWM and OPP techniques with different number of firing angles.

In order to solve the problem of low efficiency when the interleaved parallel DC-DC converters adopting current-sharing scheme work in low load conditions, this paper proposes an efficiency optimization control method which finds the load current distribution scheme for optimal efficiency by applying differential evolution (DE) algorithm and selects appropriate operation mode according to the system state. Furthermore, to test dynamic performance of different control strategies, simulations and experiments are carried out with both Dual-PI control and modified PI-PMC control based on Two-phase interleaved synchronous rectification BUCK converter. The results indicate efficiency optimization control method proposed in this paper can greatly improve the efficiency and accelerate dynamic response when adopting PI-MPC control.

One of the reason that hinders ridesharing from acceptance by general public is due to the safety concern to share a vehicle. Recently, the problem to meeting trust requirements in ridesharing systems has been formulated. As this problem is a complex discrete decision problem, it can be solved by metaheuristic algorithms. In the literature, one solution algorithm based on Particle Swarm Optimization (PSO) and another solution algorithm based on Firefly algorithm were developed to solve this problem. The goal of this study is to develop another solution algorithm based on Differential Evolution (DE) approach and compare the proposed algorithm with PSO and Firefly algorithms. Several mutation strategies in DE approach have been implement to solve the optimization problems. We evaluate the DE based algorithms by conducting experiments. We analyze the results to illustrate effectiveness of the DE based algorithms.

Various optimization objective functions have been proposed for harmonic optimization problems to eliminate unwanted specific harmonic or reduce total harmonic distortion (THD). However, the objective functions are usually intuitively selected, which lack a theoretical basis. In this paper, five objective functions with different mathematical forms are evaluated by two commonly used bio-inspired optimization algorithms, namely the particle swarm optimizers (PSO), and differential evolution (DE). By comparing the THDs, the low-order individual harmonics, and the computational time, resulted from the five objective functions, some guidelines are summarized for selecting objective functions for the bio-inspired harmonic optimization algorithms.

Parameter extraction is a challenging task, as it searches for a solution inside a high dimensional plus non-convex space. To be able to apply well known gradient based optimizers, the problem is dissected into multiple simpler yet intertwined tasks, which yields a complex and manual labour intensive procedure. On the contrary to gradient based methods, genetic algorithms perform excellent on global search problems, which eliminates the need for such a sophisticated workflow. In this paper a highly automated methodology is presented that is capable of replacing the standard manual extraction sequence for the BSIM MOSFET compact model. Due to its superior extreme finding behaviour, the Differential Evolution algorithm is applied in combination with a special error metric to ensure a high fitting quality, in all regions of the output and transfer curves. Repeatably good results for 20k measurement points are obtained, with a reduction of factor 10 in total fitting duration, while coincidentally consuming mostly computation instead of manual labour time.

A power system network has dynamic and complex characteristics. The continuity of electricity supply to customer side from the generation in stable, secure, and reliable is being a top priority to be considered. Preserving an operation of a power system on its operating point depends on its protection system scheme. Furthermore, carrying out the protection system requires a relay setting that can coordinate to carry out security. However, calculating the time dial setting (TDS) and the pickup current on each relay for practical work in industry is more difficult, complex, and takes a long time. In this paper, the directional overcurrent relay (DOCR) protection is coordinated by using hybrid particle swarm optimization - differential evolution (PSO-DE) algorithm. The proposed method is to minimize the TDS time and operating time of the relays by satisfying the protection coordination requirements. The simulation results showed that the total operating time and TMS using PSO-DE have faster operating times and TDS than other optimization algorithms.

The development of renewable energy has become massive, especially the implementation of solar panels. Even so, solar panels highly depends on irradiation levels and temperature conditions. The partial shading condition is also affects the solar panel output power. In order to get the solar panel maximum power, MPPT method can be used. This research suggests MPPT method with Self Adaptive Differential Evolution (SADE) algorithm. The PV system uses 3 large 100 Wp solar panels connected in series and a buck converter. The MPPT SADE system uses PSIM software so to test the MPPT SADE performance in order to get the maximum power output from PV Array and compared to the HPO and PSO algorithm. The results are shown that by MPPT SADE is able to do GMPP tracking in partial shading condition with average tracking accuracy and tracking period 99.99% and 0.24 seconds.

Leakages in Water Distribution Networks cause significant economical losses as well as environmental damage. Therefore, this paper proposes a novel leak location approach. The proposed method consists in using a modified differential evolution (DE) algorithm to solve the leak location task as an inverse problem based on the network’s hydraulic model. The modified DE algorithm considers the topological characteristics of the network. Temporal reasoning of the results is integrated with the proposal to improve the leak location performance. The strategy was tested under different uncertainty conditions with the model of the Modena water distribution network showing a performance ranging from 94 to 98% leak location accuracy with reduced zone sizes compared to previous approaches based on data-driven classification tools.

Under the environment of the new round of power system reform, the accounting mode of power grid is changed from price difference to cost plus income, and the competition on the power selling side is released, which greatly affects the income of regional power grids, thus greatly reducing the profit margin of power grid enterprises and greatly restricting the investment capacity. The new power reform also proposes to strengthen the overall planning of power. The investment decision-making problem of new power system is a high-dimensional, nonconvex and multi-constrained optimization problem, and the integration of wind farms further increases the difficulty of the problem. In order to optimize the problem better and enhance the convergence performance of the algorithm, based on DE(Differential Evolution) algorithm, some improvement measures such as shared fitness and adaptive adjustment of control parameters are introduced. The research results show that the improved algorithm proposed in this paper improves the convergence speed of DE algorithm, shortens the operation time to a certain extent, and obtains better optimization results, which verifies the effectiveness of the method.

In this work, the optimal design of an unmanned aerial vehicle (UAV) wing spar by multi-objective optimization genetic algorithms is studied. An electromechanical finite element model (FEM) for piezolaminated bimorph cantilever structure with embedded piezoceramics is used combined with multiobjective genetic algorithms. The FEM formulation is based on laminated plate theory combined with the first-order shear deformation theory (FSDT) for which each piezoelectric layer has one additional electrical degree of freedom. Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Generalized Differential Evolution 3 (GDE3) algorithm have been used to optimize the geometric and electric circuit parameters of a UAV generator for maximum power output and minimum mass added by the embedded piezoceramics. It is shown that the proposed algorithms are effective in developing optimal Pareto front curves for maximum electrical power output of the generator spar and minimum mass add by the embedded piezoceramics. Comparison of the generated solutions on the Pareto Front for UAV wing spar design shows that all three algorithms achieve solutions that closely resemble a constructed reference Pareto optimal front.

Optimal test sequence problem of fault diagnosis is an NP-complete problem, which inspires lots of studies. However, the multi-fault diagnosis of that is more critical and more challenging due to the overlapped signatures and the masking false failures, but this problem is very lacking in research. We propose a novel diagnostic strategy based on the major-minor testing sequences instead of the traditional fixed testing sequences. The major testing sequences lead the whole diagnosis which are evolved by an improved differential evolution algorithm (Ensemble of differential evolution variants, EDEV). Aiming at the different source faults, different minor testing sequences are generated from the major testing sequence by heuristic search algorithms. With the cooperation of these two sequences, the expected testing costs are reduced without losing the fault isolation rate. As for the hidden and masking false failures, we defined the concepts of the parent fault and fault link, so that the complexity of calculating the masking false failure is reduced from O(2mn) to O(n2), and the process of isolating multiple faults is more efficient. The study cases from real world show that the method is valid, and it is able to solve both the single-fault and multi-fault diagnosis effectively.

Evolutionary algorithms are effective tools for solving different problems such as mathematical modelling. The paper proposes an evolutionary approach to the identification of dynamic systems. It combines a genetic programming algorithm and a differential evolution method. The main modifications of the evolutionary steps for these algorithms are presented. The investigation and testing of the proposed approach was performed on problems of various dimensions.

Decision tree learning algorithms are quite old but very popular because of their efficiency. Decision trees are built sequentially from node to node, resulting in a "near-optimal" tree. This article discusses an approach to training a decision tree based on relearning already built nodes, i.e. a tree is not built sequentially, but with a return to the previous nodes. Such algorithms as Separation Measure and Differential Evolution are applied for this approach. Separation Measure selects the attributes to optimize the thresholds, and Differential Evolution performs optimization. The considered approach to learning a decision tree is compared with standard algorithms. Statistical analysis is performed to show the reliability of the results, with Student’s t-test being applied in this case. On average, the efficiency of the standard and modified decision tree learning algorithms are at the same level. However, a modified decision tree learning algorithm can find a tree that is more efficient than the trees built by the standard algorithm.

The possibility of enhancing the performance of the Probit classification model by adding a game theoretic flavour to the model parameters is explored in this paper. A game in which each attribute of the data set represents a player choosing its parameter is devised. The Nash equilibrium of this game is approximated by using a Differential Evolution algorithm. Numerical examples illustrate the potential of this approach.

Prolonging sensor and network life is a significant concern for wireless sensor networks and in this paper we present a novel optimization technique for determining the superframe slot allocation for a beacon-enabled 802.15.4 superframe operating with sensors with different data acquisition periods. The optimization approach is based on a differential evolution algorithm that leverages a flat cluster network topology to constrain the contention free period (CFP) and the contention access period (CAP) of the superframe. In this work we also define a multi-superframe structure to help conserve energy in the network. A sensitivity analysis of the optimization shows that the optimized results are not too sensitive to the clustering algorithm but are very sensitive to the original network configuration, such as number of nodes and their locations and transmission range, and therefore is better suited for static sensor networks. We also demonstrate that the optimized superframe results in a 5% gain in energy conservation over other approaches that optimize the superframe based on the ratio of the beacon order (BO) to superframe order (SO).