Self-adaptive Differential Evolution Algorithm Research Articles

Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

Self-adaptive differential evolution algorithm with dynamic fitness-ranking mutation and pheromone strategy

A Self-adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

A Self-adaptive Collaborative Differential Evolution Algorithm for Solving Energy Resource Management Problems in Smart Grids

Parameter estimation of Wiener-Hammerstein system based on multi-population self-adaptive differential evolution algorithm

PurposeThe Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.Design/methodology/approachThe idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.FindingsFrom the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.Originality/valueCompared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

Integrated Renewable Energy Storage System with Enhanced Self-Adaptive Differential Evolution Algorithm on Profit Maximization

Integrated Renewable Energy Storage System with Enhanced Self-Adaptive Differential Evolution Algorithm on Profit Maximization

Design of Novel Ultrabroadband Printed Antenna and Its Efficient Optimization Using Self-Adaptive Hybrid Differential Evolution Algorithm

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.

Self-Adaptive Differential Evolution with Gauss Distribution for Optimal Mechanism Design

Differential evolution (DE) is one of the best evolutionary algorithms (EAs). In recent decades, many techniques have been developed to enhance the performance of this algorithm, such as the Improve Self-Adaptive Differential Evolution (ISADE) algorithm. Based on the analysis of the aspects that may improve the performance of ISADE, we proposed a modified ISADE version with applying the Gauss distribution for mutation procedure. In ISADE, to determine the scaling factor (F), the population is ranked, then, based on the rank number, population size, and current generation, the formula of the Sigmoid function is used. In the proposed algorithm, F is amplified by a factor which is generated based on Gaussian distribution. It has the potential to enhance the variety of population. In comparison with several reference algorithms regarding converging speed and the consistency of optimal solutions, the simulation results reveal the performance of the suggested algorithm is exceptional.

Value aided optimal load shedding accounting voltage stability consideration employing Crow Search Algorithm with modification based on Lampinen’s criterion

This paper describes a novel load-shedding strategy that assures adequate voltage stability margin in post-load-shedding conditions. Minimum numbers of buses are selected for load shedding based on the incremental voltage of the load buses. An objective function has been formed that is to be minimized based on inequality constraints on load bus voltage and line flow. This objective function is the weighted sum of the slope of the PV-curve of the weakest bus, transmission losses, and the total load shed amount, which has been normalized with respect to the pre-load shed condition. A modified Crow Search Algorithm (CSA) has been used to obtain optimal load shed. The developed methodology has been implemented on standard IEEE 14 and 25-bus test systems, and comparisons based on statistical inferences have been carried out with the Sine–Cosine, Jaya, and Self-adaptive differential evolution (SaDE) algorithms.

Dynamic Modeling and Controller Design for a Parabolic Trough Solar Collector

The current study unveils an analytical dynamic model of a parabolic trough solar collector (PTSC) and subsequently presents the process to derive the transfer function of the PTSC system. The presented generalized transfer function derivation can be utilized to relate different operating parameters with the output temperature for any PTSC module. Presented model and derivation are applied on a PTSC module tested by Sandia National Laboratory, USA and a transfer function relating the outlet temperature of heat transfer fluid (HTF) with flowrate is derived. PID controller is designed to maintain the HTF outlet temperature by manipulating the flowrate. Controller tuning is performed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">via</i> nature-inspired algorithms (NIAs) which is rare in the context of the control systems for PTSCs. Two NIAs namely Self-adaptive Differential Evolution (SaDE) and African Vultures Optimization Algorithm (AVOA) were used for tuning with minimization of integral of time-weighted absolute error (ITAE) as an objective. It is concluded that SaDE-PID tuned controller outperforms AVOA-PID tuned controller with lower ITAE and in terms of other controller characteristics. Also, the designed controller is examined on various grounds as transient response characteristics and performance indexes. This completes the investigation of the integrity of the presented model, process, and controller.

Parameter evaluation of a nonlinear Muskingum model using a constrained self-adaptive differential evolution algorithm

Abstract The precise evaluation of the Muskingum model (MM) parameters is quite critical for routing flood waves for achieving flood control in open channels. The MM is one of the popular techniques adopted for flood routing. Estimation of the MM parameters so as to provide the best fit for the observed and computed flow values is a global optimization problem. Several optimization techniques have been adopted in the past to serve this purpose, but efficient optimization algorithms are needed to overcome the local optima issues and improvement of accuracy. In this paper, the efficiency of three optimization algorithms, namely Jaya, Covariance Matrix Adaption-Evolution Strategy (CMAES) and self-adaptive differential evolution (SaDE), has been assessed in the evaluation of the Muskingum parameters. The sum of the square deviation of the observed outflow and computed outflow (SSQ) is considered an objective in this MM optimization problem. Also, a constraint is proposed in this paper to help the optimization algorithms in finding the optimal global solutions. The simulation results show that the sum of the square deviation of the observed outflow and computed outflow (SSQ) was the least for SaDE, followed by CMAES. HIGHLIGHTS Precise evaluation of Muskingum model (MM) parameters is quite critical for routing flood waves. Efficient optimization algorithms are needed to overcome local optima issues in the estimation of the Muskingum parameters. Jaya, Covariance Matrix Adaption-Evolution Strategy (CMAES) and self-adaptive differential evolution (SaDE) have been assessed. SaDE shows the best performance followed by CMAES.

A Chaotic Sobol Sequence-based multi-objective evolutionary algorithm for optimal design and expansion of water networks

The design of a water distribution network (WDN) is an optimization problem that is computationally challenging with conflicting objectives. This study offers an enhanced Chaotic Sobol Sequence-based Multi-Objective Self-Adaptive Differential Evolution (CS-MOSADE) algorithm for multi-objective WDN design. The CS-MOSADE algorithm was tested on two benchmark WDNs, and a real WDN. Optimization results indicate that the CS-MOSADE algorithm converged two to three times faster than the MOSADE and NSGA-IIalgorithms and led to better output in terms of even distribution of solutions and convergence towards the true Pareto-optimal front. Smaller spacing metric indicated better uniformity in the obtained solutions; and larger hyper-area and coverage function values depicted better convergence towards the true Pareto-optimal front for the CS-MOSADE algorithm compared to the other algorithms. The CS-MOSADE algorithm was then applied to solve a WDN expansion problem for optimal pump scheduling and minimization of Life Cycle Cost, maximization of reliability and minimization of Green House Gas (GHG) emissions. A significant reduction in GHG emissions of 2.17 x 106 kg was achieved at an additional cost of $0.55 x 107 when optimal pump scheduling was incorporated in the model of the real WDN over service life of 50 years.

A Self-Adaptive Multiobjective Differential Evolution Algorithm for the Unrelated Parallel Batch Processing Machine Scheduling Problem

In this paper, the unrelated parallel batch processing machine (UPBPM) scheduling problem is addressed to minimize the total energy consumption (TEC) and makespan. Firstly, a mixed-integer line programming model (MILP) of the UPBPM scheduling problem is presented. Secondly, a self-adaptive multiobjective differential evolution (AMODE) algorithm is put forward. Since the parameter value can affect the performance of the algorithm greatly, an adaptive parameter control method is proposed according to the convergence index of the individual and the evolution degree of the population to improve the exploitation and exploration ability of the algorithm. Meanwhile, an adaptive mutation strategy is proposed to improve the algorithm’s convergence and the solutions’ diversity. Finally, to verify the effectiveness of the algorithm, comparative experiments are carried out on 20 instances with 5 different scales. Numerical comparisons indicate that the proposed method can achieve high comprehensive performance.

An Overview of Self-Adaptive Differential Evolution Algorithms with Mutation Strategy

Differential Evolution (DE) is a widely used global searching algorithm that solves real-world optimization problems. It is categorized as a stochastic parameter optimization method that has a broad spectrum of applications, notably neural networks, logistics, scheduling, and modeling. In practice, different optimization issues need different parameter settings. Due to DE simplicity, ease of implementation, and dependability, many scientists were interested in examining this algorithm. Nonetheless, the quality of DE and its variations are directly influenced by different mutation techniques and control parameter settings. In this paper, an overview and analogy of some algorithms that employ different mutation techniques will be illustrated. Additionally, a novel strategy that uses different mutation methods is proposed and compared with some existing strategies.

Comparison of coal-to-ethanol product separation strategies

Given China's energy structure and the limitations of bioethanol, the coal-to-ethanol (CTE) pathway, from dimethyl ether to ethanol (DMTE) via carbonylation and hydrogenation, is highly anticipated. Ethanol, methanol, methyl acetate, and ethyl acetate are the crude hydrogenation products that need to be purified, requiring at least an eight-column scheme. However, the optimization of the existing separation strategy with ethanol as the priority is unfavorable in the following aspects: it is usually plagued by tedious rules of thumb and, due to the large scale of the process, is prone to falling into local minima; pre-designed heat integration inevitably neglects the interaction of parameter optimization and heat integration; reports on alternative feasible distillation sequences are scarce in publications, let alone comparisons amongst these counterparts. Therefore, four viable separation strategies are proposed in this paper to compare with this faulted separation strategy. A self-adapting dynamic differential evolution (SADDE) algorithm, which is accelerated by parallel computation, is used to search for optimal column parameters of all the configuration options and facilitates simultaneous heat integration structure synthesis. Two strategies stand out after 3000 generations of evolution. Splitting methanol outperforms in specific steam consumption (SSC) of ethanol (1.8177), much better than the benchmark (2.4840), and splitting ethyl acetate with ethyl acetate priority has the most competitive total annual cost (TAC), 23.98% lower than the benchmark.In summary, this paper provides a reference for optimizing complex distillation systems like CTE product separation, or more specifically, the DMTE route, before the appearance of the most suitable separation strategy in demand. Furthermore, it will also serve for the CTE superstructure to further explore the optimal distillation sequence.

An efficient bi-objective approach for dynamic economic emission dispatch of renewable-integrated microgrids

To overcome the challenges of conventional power systems, such as increasing power demand, requirements of stability and reliability, and increasing integration of renewable energy sources, the concept of microgrids was introduced and is currently one of the most important solutions for solving the mentioned problems. Generally, microgrids have two operating modes, namely grid-connected and islanded modes. Based on the literature and its unique characteristics, the islanded mode is more challenging than the other one. In this paper, a new self-adaptive comprehensive differential evolution (SACDE) algorithm is proposed for solving economic load dispatch (ELD) and combined economic emission dispatch (CEED) problems, achieving optimal power consumption in isolated microgrids. Initially, SACDE is employed for solving the ELD problem as a single-objective function, meaning that the operational cost is just considered as the objective function, and thereby, the resources are scheduled accordingly. Then, a multi-objective platform based on SACDE is also proposed to solve the CEED problem. It means two objective functions, including operational cost and emission, are simultaneously optimized. For evaluating the performance of the proposed method, three different scenarios under various cases are considered. According to the results, when SACDE is employed to solve the single objective function (cost minimization) problem, it has better performance than other methods. In terms of the bi-objective scheme (cost and emission minimization), SACDE is significantly superior to the price penalty factor technique which is frequently used in previous studies.

Nonlinear Covariance Analysis-Based Robust Rendezvous Trajectory Design by Improved Differential Evolution Method

This paper presents a robust trajectory design method for approaching and rendezvous with a space target considering multi-source uncertainties. A nonlinear covariance analysis method based on the state transition tensor is presented to formulate the propagation of uncertainties including environment parameter uncertainty, actuator error, sensor noise, navigation error and initial state dispersion of the closed-loop GN&amp;C system. Then, the robust trajectory design problem is defined based on the quantified effect of the uncertainties, and an improved self-adaptive differential evolution algorithm is presented to solve the robust trajectory design problem with uncertainties. Finally, four groups of numerical simulations are carried out to show that the designed robust trajectories can satisfy the final state dispersion constraint under multi-source uncertainties.

Improved opposition-based self-adaptive differential evolution algorithm for vibrational hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering thermometry.

We propose an improved opposition-based self-adaptive differential evolution (IOSaDE) algorithm for multi-parameter optimization in vibrational hybrid femtosecond/picosecond coherent anti-Stokes Raman scattering (CARS) thermometry. This new algorithm self-adaptively combines the advantages of three mutation schemes and introduces two opposite population stages to avoid premature convergence. The probability of choosing each mutation scheme will be updated based on its previous performance after the first learning period. The IOSaDE method is compared with nine other traditional differential evolution (DE) methods in simulated spectra with different simulation parameters and experimental spectra at different probe time delays. In simulated spectra, both the average and standard deviation values of the final residuals from 20 consecutive trials using IOSaDE are more than two orders of magnitude smaller than those using other methods. Meanwhile, the fitting temperatures in simulated spectra using IOSaDE are all consistent with the target temperatures. In experimental spectra, the standard deviations of the fitting temperatures from 20 consecutive trials decrease more than four times by using IOSaDE, and the errors of the fitting temperatures also decrease more than 18%. The performance of the IOSaDE algorithm shows the ability to achieve accurate and stable temperature measurement in CARS thermometry and indicates the potential in applications where multiple parameters need to be considered.

Multiobjective Automated Type-2 Parsimonious Learning Machine to Forecast Time-Varying Stock Indices Online

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model’s explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs’ are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm’s performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.

Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems

With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.

A hybrid differential evolution algorithm for flexible job shop scheduling with outsourcing operations and job priority constraints

Owing to the increasing complexity of products and the specialization of enterprises, production outsourcing has become a common practice in industrial manufacturing. Moreover, different jobs feature various priorities in actual production. The previous research that aims to minimize the makespan may not be applicable in real scenarios. Therefore, this study investigates a flexible job shop scheduling problem with outsourcing operations and job priority constraints. We propose a sequence-based mathematical model aiming at minimizing weighted overdue days, which considers the outsourcing constraints and different overdue weights of jobs with different priorities. An efficient hybrid self-adaptive differential evolution algorithm with heuristic strategies (HSDE) is proposed to address this problem. In HSDE, a well-designed chromosome encoding and decoding method is presented. To eliminate individuals that do not satisfy the outsourcing constraints, we add a penalty term to improve the objective function. By considering heuristic strategies for initial chromosome generation, the proposed approach is able to achieve a high-quality initial population. Crossover and mutation operators with self-adaptive control of the parameters are established to enlarge the search range and accelerate the convergence speed. Finally, several experiments are conducted to verify the effectiveness of the proposed model and algorithm. Experimental results confirm that the proposed algorithm outperforms other algorithms both in efficiency and accuracy.

Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems.