Self-adaptive Differential Evolution Research Articles

A Self-Adaptive Multi-Objective Differential Evolution Algorithm Applied to the Styrene Reactor Optimization

A Self-Adaptive Multi-Objective Differential Evolution Algorithm Applied to the Styrene Reactor Optimization

Optimization of Different Deployments and Resolution of Antenna Arrays in SWIPT

In this paper, three different deployment antenna arrays with circular, triangular and rectangular shapes were used to optimize the simultaneous wireless information and power transfer (SWIPT) system for the Internet of Things (IoT). Ray-tracing was employed to channel the model for a real environment. Self-adaptive dynamic differential evolution (SADDE) was used to optimize the harvesting power ratio with bit error rate constrained by the two different resolutions of feed length (high resolution and low resolution). Numerical results show that those three antenna arrays can achieve the goal for information quality in both resolutions. The harvesting power ratio for the circular array is the best and the harvesting power ratio for the rectangular array is the worst. The harvesting power ratio for the low-resolution case is 25% lower than the high-resolution case. However, the circular antenna array is the best deployment in those three different arrays for both high and low resolutions.

Advances in Evolutionary Optimization of Quantum Operators

A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices.A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices. Three existing techniques (representations) which allow generating unitary matrices are used in various evolutionary algorithms in order to optimize their coefficients. The objective is to obtain as precise quantum operators (the resulting unitary matrices) as possible for given quantum transformations. Ordinary evolution strategy, self-adaptive evolution strategy and differential evolution are applied with various settings as the optimization algorithms for the quantum operators. These algorithms are evaluated on the tasks of designing quantum operators for the 3-qubit and 4-qubit maximum amplitude detector and a solver of a logic function of three variables in conjunctive normal form. These tasks require unitary matrices of various sizes. It will be demonstrated that the self-adaptive evolution strategy and differential evolution are able to produce remarkably better results than the ordinary evolution strategy. Moreover, the results can be improved by selecting a proper settings for the evolution as presented by a comparative evaluation.

A Hybrid and Self-Adaptive Differential Evolution Algorithm for the Multi-Depot Vehicle Routing Problem in Egg Distribution

This paper presents the Hybrid and Self-Adaptive Differential Evolution algorithms (HSADE) to solve an egg distribution problem in Thailand. We introduce and formalize a model for a multi-product, multi-depot vehicle routing problem with a time window, a heterogeneous fleet and inventory restrictions. The goal of the problem is to minimize the total cost. The multiple products comprise customers’ demands with different egg sizes. This paper presents a Mixed Integer Linear Programming (MILP) model, an initial solution-based constructive heuristic, a new self-adaptive mutation strategy, and a neighborhood search structure with the probability to improve DE. The two measurements of criteria are the heuristic performance (HP) compared with the solution obtained by MILP and the relative improvement (RI) of the solution compared with Thailand’s current egg distribution practice. The computational results show that the performance of HSADE is better than the current practice, and HSADE can provide on average a 14.13% improvement in total cost. Additionally, our proposed algorithm can be applied to similar agriculture logistics in Thailand and worldwide.

Self-Adaptive Multi-Task Differential Evolution Optimization: With Case Studies in Weapon–Target Assignment Problem

Multi-task optimization (MTO) is related to the problem of simultaneous optimization of multiple optimization problems, for the purpose of solving these problems better in terms of optimization accuracy or time cost. To handle MTO problems, there emerges many evolutionary MTO (EMTO) algorithms, which possess distinguished strategies or frameworks in the aspect of handling the knowledge transfer between different optimization problems (tasks). In this paper, we explore the possibility of developing a more efficient EMTO solver based on differential evolution by introducing the strategies of a self-adaptive multi-task particle swarm optimization (SaMTPSO) algorithm, and by developing a new knowledge incorporation strategy. Then, we try to apply the proposed algorithm to solve the weapon–target assignment problem, which has never been explored in the field of EMTO before. Experiments were conducted on a popular MTO test benchmark and a WTA-MTO test set. Experimental results show that knowledge transfer in the proposed algorithm is effective and efficient, and EMTO is promising in solving WTA problems.

Self-adaptive multi-objective differential evolution algorithm with first front elitism for optimizing network usage in networked control systems

In networked control systems, by means of event-triggered transmission, it is possible to reduce the network usage, keeping the control system performance at satisfactory levels. There are several schemes for event-triggered transmission. In this study, we propose a multi-objective optimization problem to tune the event-triggered mechanisms. On solving the proposed problem by means of multi-objective evolutionary optimization, a set of efficient solutions is generated with different tradeoffs between control system performance and the number of transmissions. To solve the proposed problem, we also developed an improved multi-objective differential evolution algorithm that includes a self-adaptive mechanism, dynamic crowding distance operator, and novel elitism of the first front. The proposed method is applied to tune decentralized event-triggered mechanisms for a controller given a priori, considering random network-induced delays and packet loss. Two case studies are present ed, comparing the performance of eight different decentralized event-triggered schemes, analyzing the selection of the sampling period, and demonstrating the efficacy of the proposed tuning method.

SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

The differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.

A Modified Binary Firefly Algorithm to Solve Hardware/Software Partitioning Problem

Hardware/Software (Hw/Sw) partitioning is a crucial step in Hw/Sw co-design that determines which components of the embedded system could be implemented on hardware and which ones on software. It aims to find a design implementation that fulfils all the specification requirements (functionality, goals and constraints) with a low cost. Most formulations of Hw/Sw partitioning dilemma have proven to NP-hard optimization problem. The firefly algorithm (FA) emerges as a significant tool of Swarm Intelligence that has been applied in many areas of optimization. The main purpose of this paper is to present a modified binary firefly algorithm to solve Hw/Sw partitioning problems. Results are compared with other well-known metaheuristic algorithms: Binary Differential Evolution (BDE) , Discrete Differential Evolution(DDE), Self-adaptive differential evolution algorithm (saBDE), New Modified Binary Differential Evolution (NMBDE), New Binary Differential Evolution(NBDE) , Particle Swarm Optimization Algorithm (PSO), and Genetic Algorithm (GA). The computational results show that it produced better results than the all used algorithms.

Radial Basis Function Neural Network Optimal Modeling for Phase-Only Array Pattern Nulling

Phase-only nulling with low sidelobe level is a problem of interest in array synthesis which is a tedious problem without an analytical solution. In this communication, a novel framework for the phase-only nulling based on radial basis function neural network (RBFNN) is proposed to predict the phase adjustment for the array pattern nulling with sidelobe control. In the process of network training, the parameters of the RBFNN are optimized simultaneously based on the self-adaptive differential evolution (SADE) algorithm, which aims to improve the approximation ability and reduce the complexity of the network. Simulation results of the optimized RBFNN models show compact network structure and form the array pattern under desired performance with good generalization capability.

Self-adaptive resources allocation-based differential evolution for constrained evolutionary optimization

When using evolutionary algorithms to address constrained optimization problems, it is important to balance not only the diversity and convergence but also the constraints and objective function. To this end, a self-adaptive resources allocation-based differential evolution (SRADE) is presented in this paper. Specifically, during the evolutionary process, three mutation strategies with distinct focuses are collaboratively employed and adaptively assigned to different individuals based on their performance feedback. That is, most of the computing resources will be consumed by the most efficient strategy at different evolutionary stages to mitigate inefficient search under limited resources. These three collaborative strategies focus on maintaining population diversity, driving the population into feasible regions, and promoting the population toward the objective, respectively. Combining the self-adaptive resources allocation scheme and diverse search strategies is expected to satisfy the requirements of the population for diversity, convergence, constraints, and the objective at a certain iteration. Extensive experiments are performed on three benchmark test suites, including a large number of test functions from IEEE CEC 2006, 2010, and 2017. Compared to other well-designed constrained evolutionary approaches, SRADE exhibits superior or very competitive performance.

Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge

State of charge (SOC) estimation is a significant task for lithium-ion batteries. However, the accuracy of SOC estimation is closely related to parameters of battery and system non-linearity. To identify the parameters of lithium-ion battery better, this proposed a self-adaptive particle swarm optimization differential evolution (SaPSODE) algorithm. First, to describe the dynamic behaviors of battery, we presented a first-order RC equivalent circuit model (ECM). Second, to calculate open-circuit voltage (OCV) versus time during the dynamic test procedure, an optimizing objective function was built to minimize errors between the true and optimized terminal voltages. Further, control parameters of F and Cr were also self-adapted according to previous successful records. Third, by using OCV-SOC mapping curves, this work obtained estimated SOC curve which was compared with true SOC curve. Comprehensive experimental results demonstrated effectiveness of the proposed framework and methodology, compared with several highly-cited DE variants.

Global optimization of a vapor compression refrigeration system with a self-adaptive differential evolution algorithm

This paper presents a global optimization strategy for the vapor compression refrigeration system (VCRS) based on self-adaptive differential evolution (SADE) algorithm to minimize the system energy consumption with the indoor cooling requirements fulfilled. The simplified hybrid VCRS model is established based on the thermodynamics and heat transfer theory, together with the parameter regression method, to predict the system performance and energy consumption under different working conditions. Then considering the interactions between components, the influences of varying indoor loads and outdoor air conditions, a global optimization problem with constraints is formulated. The SADE algorithm is used to solve the global optimization problem, and the optimal variables settings are obtained. Simulation results demonstrate that the proposed global optimization strategy can improve the VCRS energy efficiency in different time periods while satisfying the cooling demands. Averagely 15.57% energy saving on the typical testing day can be achieved. Moreover, significant energy savings can be obtained during the morning and evening periods with partial indoor cooling loads. Furthermore, to show the effectiveness of proposed SADE strategy, the DE and classical PSO algorithms are compared, and the results indicate that the SADE algorithm can significantly reduce the calculation time, and can avoid local minimum. This research brings a feasible methodology, to reduce the energy consumption of air conditioning systems effectively.

China's coal consumption forecasting using adaptive differential evolution algorithm and support vector machine

Coal consumption forecasting is the premise of energy supply structure reform and the basic work of energy planning under the background of climate change. Based on the econometrics which determines the influencing factors, this paper proposes a combined algorithm using Self-adaptive differential evolution (SaDE) algorithm and support vector (SVM) optimization algorithm for coal consumption forecasting. The optimization algorithm reduces the selection problem of SVM oversized hyperplane parameters, improves its global optimization ability, and further improves the prediction accuracy of SVM. The results of China's coal consumption forecasting show that the improved SaDE-SVM algorithm has good adaptability, robustness, faster converging speed and higher prediction accuracy for the prediction of less data samples and multiple influencing factors suitable for relevant medium and long term forecasts. The forecast shows that China's coal consumption is expected to be between 3.92 billion tons and 4.14 billion tons by 2020. By 2030, China's coal consumption will be between 2.195 billion tons and 3.699 billion tons.

An Ultra-Fast Power Prediction Method Based on Simplified LSSVM Hyperparameters Optimization for PV Power Smoothing

With existing power prediction algorithms, it is difficult to satisfy the requirements for prediction accuracy and time when PV output power fluctuates sharply within seconds, so this paper proposes a high-precision and ultra-fast PV power prediction algorithm. Firstly, in order to shorten the optimization time and improve the optimization accuracy, the single-iteration Gray Wolf Optimization (SiGWO) method is used to simplify the iteration process of the hyperparameters of Least Squares Support Vector Machine (LSSVM), and then the hybrid local search algorithm composed of Iterative Local Search (ILS) and Self-adaptive Differential Evolution (SaDE) is used to improve the accuracy of hyperparameters, so as to achieve high-precision and ultra-fast PV power prediction. The power prediction model is established, and the proposed algorithm is applied in a test experiment which can complete the power prediction within 3 s, and the RMSE is only 0.44%. Finally, combined with the PV-storage advanced smoothing control strategy, it is verified that the performance of the proposed algorithm can satisfy the system’s requirements for prediction accuracy and time under the condition of power mutation in a PV power generation system.

Dynamic soft sensor modeling method fusing process feature information based on an improved intelligent optimization algorithm

The lack of real-time and accurate melting index forecast model puts forward challenges to the online quality detection and control of the polypropylene industrial production. Meanwhile, the key process characteristics of dynamic and time delay are not fully and effectively integrated into the soft sensor model. Therefore, a novel dynamic soft sensing (DSS) modeling method using the orthogonal echo state network (OESN) based on the cumulative mutual information (CMI) threshold setting integrating an improved differential evolution algorithm (IDE) (OESN-CMI-IDE) is proposed. The mutual information of each candidate auxiliary variable relative to the dominant variable is calculated and sorted to determine the CMI threshold and select the corresponding auxiliary variable. Then an IDE algorithm is used to optimize the parameters of the OESN model, which has faster convergence, higher accuracy and stronger robustness than that of DE, self-adaptive DE (jDE), DE with time varying scale factor (DETVSF), line decreased inertia weight particle swarm optimization (LDPSO), flexible exponential inertia weight particle swarm optimization (FEPSO), and oscillating triangular inertia weight particle swarm optimization (OTPSO) in terms of evolutionary computing competition benchmark function tests. Finally, the proposed method is applied to predict the melt index in an actual polypropylene plant. The experimental results demonstrate that the prediction effect of the proposed method is better than that of the OESN model optimized by DE, jDE, DETVSF, LDPSO, FEPSO, OTPSO and the OESN model without optimization, which can escort the online monitoring and advanced control of the melt index in the propylene industry.

Modeling and optimization of VEAM of 10% B4C/Al metal matrix composite using ANN-SCA approach

Shaping advanced materials such as superalloys and metal matrix composites is still a herculean task for the engineering fraternity. People are developing innovative manufacturing processes (MPs) to shape such kind of advanced materials. Electrical arc machining (EAM) is one such subtractive MP which is under development stage. The present paper discusses one newly developed advanced machining process named as vibration assisted EAM (VEAM). The machining on metal matrix composite has been performed using VEAM. Two of the performance indicators that are material removal rate (MRR) and tool wear rate has been explored during machining using VEAM. Experimental findings indicate that VEAM results in significantly enhanced MRR as compared to conventional EDM. At the last the single objective optimization has been done by using hybrid artificial neural network & four advanced optimization algorithms such as self-adaptive differential evolution, shuffled frog leaping algorithm, coordinated aggregation based particle swarm optimization and sine cosine algorithm (SCA). It has been observed that SCA demonstrate better performance as compared to its peers.

Investigation of electronic transport in InAs/GaAs samples. A study using the metaheuristic self-adaptive differential evolution method

This manuscript presents the study of electronic transport on a set of five multilayer molecular beam epitaxy-grown InAs/GaAs semiconductor samples. We developed an automated switch system to carry out electronic transport measurements of mobility and carrier concentration using the van der Pauw technique. Measurements were carried out as a function of temperature within the range of 260 K to 310 K. To identify which scattering mechanisms most contributed to mobility limitation, It was necessary to use the Self-adaptive Differential Evolution meta-heuristic method. This method allowed the determination of the main scattering mechanisms limiting the electronic mobility and identified as scattering by dislocations and phonons. Dislocations consist of the dominant defects in this lattice mismatch structure. Therefore, to increase carrier mobility, we propose some strategies: a change in the sample growth parameters such as substrate temperature and InAs/GaAs layer thickness. Alternatively, annealing of the samples could also be considered to improve sample mobility.

Different Object Functions for SWIPT Optimization by SADDE and APSO

Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.

A Self-Adaptive Hybrid Bat Algorithm for Training Feedforward Neural Networks

Training feedforward neural network (FFNN) is a complex task in the supervised learning field. An FFNN trainer aims to find the best set of weights that minimizes classification error. This paper presents a new training method based on hybrid bat optimization with self-adaptive differential evolution to train the feedforward neural networks. The hybrid training algorithm combines bat and the self-adaptive differential evolution algorithm called BAT-SDE. BAT-SDE is used to better search in the solution space, which proves its effectiveness in large space solutions. The performance of the proposed approach was compared with eight evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using seven bio-medical datasets and one large credit card fraud detection dataset. The results of the comparative study show that BAT-SDE outperformed other training methods in most datasets and can be an alternative to other training methods.

An improved dynamic multi-objective optimization approach for nonlinear equation systems

Solving nonlinear equation systems using evolutionary algorithms involves solving two key problems. One problem is how to efficiently optimize the nonlinear equations derived from the physical features, and the other problems is how to locate more than one optimal solution in a single trial. To address these two problems, an improved dynamic tri-objective differential evolution method is proposed in this paper. First, we transform a given system with any type and number of nonlinear equations into a dynamic tri-objective optimization problem, which targets the first problem. Second, we develop a self-adaptive ranking multi-objective differential evolution, which targets the second problem. In addition, a probability distribution-based local search is introduced, which aims to identify the optimal solutions with a high level of accuracy. Based on previous studies of numerical optimizations with multiple solutions, each component is elaborately proposed and developed, so it is more suitable for a nonlinear equation system. Experiments were conducted on 30 numerical examples collected from real-world applications. The statistical results are encouraging, showing that the performance of the proposed approach is better than that of eight state-of-the-art evolutionary algorithms, with respect to root ratio and success rate metrics.