Opposition-based Differential Evolution Research Articles

Lens imaging opposition-based learning for differential evolution with cauchy perturbation

Opposition-based learning (OBL) is an effective optimization strategy that enhances the performance of various global optimization algorithms. Among these algorithms, differential evolution (DE) based on OBL has received the attention of many researchers. However, OBL is a strategy with relative symmetry between the target point and the opposite point, which does not contribute significantly to the population in the later stage of the algorithm, and tends to disregard more meaningful points around the opposite points. Therefore, we propose a novel opposition-based differential evolution based on the principle of convex lens (LensOBLDE), which utilizes the distance relationship between the target point and the focal length of the lens, and dynamically adjusts the search radius by controlling the parameters, thus further improving the search ability. In addition, to avoid the algorithm from falling into a local optimum, a small-scale and low-probability cauchy perturbation strategy is applied to the elite individuals in the population, which ensures the exploration and exploitation of the algorithm. Finally, LensOBLDE evaluates the performance of several advanced algorithms on the CEC2017 benchmark suite and conducts a statistical test to evaluate the difference between algorithms. The results indicate that LensOBLDE outperforms several algorithms with a significant difference between them. This verifies the effectiveness of the strategies newly added in this study.

Evolutionary Architecture Optimization for Retinal Vessel Segmentation.

Retinal vessel segmentation (RVS) is crucial in medical image analysis as it helps identify and monitor retinal diseases. Deep learning approaches have shown promising results for RVS, but designing optimal neural network architecture is challenging and time-consuming. Neural architecture search (NAS) is a recent technique that automates the design of neural network architectures within a predefined search space. This study proposes a new NAS method for U-shaped networks, MedUNAS, that discovers deep neural networks with high segmentation performance and lower inference time for RVS problem. We perform opposition-based differential evolution (ODE) and genetic algorithm (GA) to search for the best network structure and compare discrete and continuous encoding strategies on the proposed search space. To the best of our knowledge, this is the first NAS study that performs ODE for RVS problems. The results show that the MedUNAS ODE and GA yield the best and second-best results regarding segmentation performance with less than 50% of the parameters of U-shaped state-of-the-art methods on most of the compared datasets. In addition, the proposed methods outperform the baseline U-Net on four datasets with networks with up to 15 times fewer parameters. Furthermore, ablation studies are performed to evaluate the generalizability of the generated networks to medical image segmentation problems that differ from the trained domain, revealing that such networks can be effectively adapted to new tasks with fine-tuning. The MedUNAS can be a valuable tool for automated and efficient RVS in clinical practice.

Opposition-based differential evolution for synchronized control of multi-agent systems with uncertain nonlinear dynamics

Multi-agent systems (MAS) have gained increasing research attention in recent years due to their wide applications in cooperative control of autonomous vehicles, sensor networks, robotics, etc. However, designing effective distributed control strategies for MAS with complex dynamics remains challenging. This paper proposes an improved differential evolution (DE) based cooperative control strategy to stabilize and synchronize MAS with uncertain nonlinear dynamics. Specifically, we develop a new mutation strategy inspired by opposition-based learning to enhance the exploration ability and adopt an adaptive parameter mechanism to balance exploration and exploitation. Moreover, a distributed sliding mode term is incorporated in the control input to counteract uncertainties and external disturbances. Rigorous stability analysis shows that the closed-loop MAS can achieve synchronization under the proposed control strategy. Extensive simulations demonstrate that compared to other state-of-the-art DE algorithms and cooperative control methods, the proposed strategy achieves lower synchronization error, control cost, higher convergence speed, and higher accuracy for MAS with different scales, dynamics, and uncertainties.

Differential evolution-based neural architecture search for brain vessel segmentation

Brain vasculature analysis is critical in developing novel treatment targets for neurodegenerative diseases. Such an accurate analysis cannot be performed manually but requires a semi-automated or fully-automated approach. Deep learning methods have recently proven indispensable for the automated segmentation and analysis of medical images. However, optimizing a deep learning network architecture is another challenge. Manually selecting deep learning network architectures and tuning their hyper-parameters requires a lot of expertise and effort. To solve this problem, neural architecture search (NAS) approaches that explore more efficient network architectures with high segmentation performance have been proposed in the literature. This study introduces differential evolution-based NAS approaches in which a novel search space is proposed for brain vessel segmentation. We select two architectures that are frequently used for medical image segmentation, i.e. U-Net and Attention U-Net, as baselines for NAS optimizations. The conventional differential evolution and the opposition-based differential evolution with novel search space are employed as search methods in NAS. Furthermore, we perform ablation studies and evaluate the effects of specific loss functions, model pruning, threshold selection and generalization performance on the proposed models. The experiments are conducted on two datasets providing 335 single-channel 8-bit gray-scale images. These datasets are a public volumetric cerebrovascular system dataset (vesseINN) and our own dataset called KUVESG. The proposed NAS approaches, namely UNAS-Net and Attention UNAS-Net architectures, yield better segmentation performance in terms of different segmentation metrics. More specifically, UNAS-Net with differential evolution reveals high dice score/sensitivity values of 79.57/81.48, respectively. Moreover, they provide shorter inference times by a factor of 9.15 than the baseline methods.

Measurement-based ZIP load modelling using opposition based differential evolution optimization

With the large integration of distributed generations, power grids are on the verge of instability if the generation and load side is not synchronized properly. To predict the load, a proper load model should be developed for the purpose of power system monitoring. The ZIP load model is considered in this work due to its simplicity. The parameter of the ZIP load corresponding to constant power, constant impedance, and constant current load must be estimated accurately for predicting the load behavior. In this paper, the measurements are being compared with the estimated value for estimating the load parameters using a suitable parameter tuning method. For ZIP load modeling Measurement-based parameter estimation is applied. In this paper, measurements are simulated by statistically infusing the Gaussian noise into the true value (obtained from the load flow study). The parameter-tuning algorithm iteratively feeds the error discovered from the comparison to determine the optimal solution. In this paper, Opposition-based differential evolution optimization (ODEO) is being proposed as a parameter tuning method for estimating the load parameters from the measurements collected at a given load bus. Measurement of the ZIP load is collected at bus 30 of the NE 39 Bus system after applying step disturbance of voltage at generator 4. The proposed ODEO method is validated and compared with the other tuning methods, like Particle Swarm optimization (PSO) and Grey Wolf optimization (GWO). The proposed ODEO method has a better performance as compared to PSO and GWO.

An opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization

Clustering has grown to be a research focus in recent years owing to the challenges of labeling massive collected data. Recent advances such as the emotional preference and migratory behavior clustering model jointly utilized the biogeography-based idea and inertia theorem for handling data clustering tasks. However, as with most clustering methods, the initialization for the solution is often purely random. Moreover, the search results vulnerably converge to local optima due to favorable exploration but short exploitation, which requires an additional process to balance them effectively. Inspired by the differential evolution (DE) strategy and the opposition-based learning (OBL) method, the paper proposed a novelty opposition-based differential evolution clustering algorithm for emotional preference and migratory behavior optimization named OBDE-EPMC. Specifically, we first utilize the OBL method to initialize the population to obtain a closer initial individual to the optimal solution. Thereafter, to ensure population diversity during the optimization, we applied the differential evolution (DE) strategy to expand the scope of the solution and the opposition-based learning method to generate an opposite one. Lastly, the historical solution scheme is added to retain the excellent solution within the population. In addition, the theoretical analysis and convergence property proof of OBDE-EPMC are presented. Numerous experiments were performed to compare the proposed OBDE-EPMC with the other seven clustering algorithms on a host of standard datasets. And several standards demonstrate the rationality and effectiveness of our proposed OBDE-EPMC algorithm.

Self-adaptive opposition-based differential evolution with subpopulation strategy for numerical and engineering optimization problems

Opposition-based differential evolution (ODE) is a well-known DE variant that employs opposition-based learning (OBL) to accelerate the convergence speed. However, the existing OBL variants are population-based, which causes many shortcomings. The value of the jumping rate is not self-adaptively adjusted, so the algorithm easily traps into local optima. The population-based OBL wastes fitness evaluations when the algorithm converges to sub-optimal. In this paper, we proposed a novel OBL called subpopulation-based OBL (SPOBL) with a self-adaptive parameter control strategy. In SPOBL, the jumping rate acts on the individual, and the subpopulation is selected according to the individual’s jumping rate. In the self-adaptive parameter control strategy, the surviving individual’s jumping rate in each iteration will participate in the self-adaptive process. A generalized Lehmer mean is introduced to achieve an equilibrium between exploration and exploitation. We used DE and advanced DE variants combined with SPOBL to verify performance. The results of performance are evaluated on the CEC 2017 and CEC 2020 test suites. The SPOBL shows better performance compared to other OBL variants in terms of benchmark functions as well as real-world constrained optimization problems.

Optimal scheduling with opposition based differential evolution optimized fixed head hydro-thermal power system

This research incorporates an optimization techniques Opposition-based Differential Evolution (ODE) for estimating optimum hourly energy generation scheduling of a hydro-thermal system. In order to increase the efficacy and quality of the solution the ODE is used. The suggested ODE uses opposition-based learning(OBL) to initialize population as well as for enhancing generation. Two fixed head hydrothermal tests have been taken to verify the efficacy of the suggested technique. These results are contrasted with the results of other techniques of evolution. It has been concluded that the proposed technique is capable of providing a better solution.

Neighborhood opposition-based differential evolution with Gaussian perturbation

Neighborhood opposition-based differential evolution with Gaussian perturbation

HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network

Abstract For cloud providers, workload prediction is a challenging task due to irregular incoming workloads from users. Accurate workload prediction is essential for scheduling the resources to the cloud applications. Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long Short-Term Memory (LSTM) network and a multilayer perceptron network. By improving the traditional LSTM architecture by using opposition-based differential evolution algorithm and dropout technique on recurrent connection without memory loss, the proposed approach has the ability to perform a better prediction process. A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. Finally, the authors measure the proposed approach’s effectiveness under benchmark data sets of NASA and Saskatchewan servers. The experimental results proved that the proposed approach outperforms the other conventional methods.

RODE: Ranking-Dominance-Based Algorithm for Many-Objective Optimization with Opposition-Based Differential Evolution

During the period of 1990s and early 2000s, the Pareto-dominance (PD) relation was successfully applied for solving multiobjective optimization problems (MOPs) with small number of objectives (typically not exceeding four objectives). However, the performance of these PD-based multiobjective evolutionary algorithms (MOEAs) becomes hopeless when it comes to solving problems with larger number of objectives. Many alternative dominance relations have been proposed in the last few years to improve the search ability of EMO algorithms. In this paper, we present the RODE algorithm as a novel scalable approach for many-objective problems which adopts the ranking-dominance relation for evaluating the fitness of solutions that provides improved convergence. The evolutionary search mechanism employed in our algorithm is the conventional differential evolution (DE) approach. However, for attaining the improved diversity of solutions, we have incorporated the weight vectors and opposition-based differential evolution (ODE) in a unique way. In order to validate our RODE approach, we have compared it with other state-of-the-art MOEAs, namely GDE3, NSGAII and two versions of MOEA/D, namely MOEA/D-TCH and MOEA/D-PBI. All the MOEAs have been executed on the standard benchmark problems DTLZ1-DTLZ4 with 5-, 8-, 10-, 15-, and 20-D objective spaces and MaF03, MaF05 and MaF06 with 8-, 10- and 15-D objective spaces. In almost all the simulation experiments (especially with higher than 5-objectives), our approach has achieved promising results in terms of convergence and diversity of solutions.

Implementation of differential evolution algorithm and its variants for optimal scheduling of distributed generations

SummaryModern power systems are being shifted its attention towards distributed generations (DGs). DGs have allured more concern as an auspicious opportunity with considerable economic profits. To minimize the power generation costs of DGs, day‐to‐day operation scheduling is essential. The role of this study is to offer an optimal operation schedule for DG with several energy sources including renewable energy sources (RES), considering economic facets. In order to achieve the cost minimization along with optimal scheduling, an objection function has been formulated and solved using the optimization algorithms. This study aims to present the applications of differential evolution (DE) algorithm and its variants such as opposition‐based differential evolution (ODE), self‐adaptive differential evolution (SaDE), improved differential evolution (IDE), and cultivated differential evolution (CuDE) for scheduling DG optimally. A scheme for optimal scheduling of thermal, wind power, and solar PV generators has been evaluated. The simulations have been carried out on IEEE 14 bus system, IEEE 30 bus system, IEEE 57 bus system, and Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO), as a real part of 62 bus Indian utility system (IUS). The novelty of this study lies in simulating a real‐time system for solving optimal scheduling problem, in that way helping decision makers to choose the optimal operation points. The results indicate that the SaDE outperformed other DE variants by giving the best fitness value and convergence rate.

Exploring the Reasons Behind the Good Performance of Opposition-Based Learning

In the face of these diverse forms of opposition existed widely in real-world contexts, as a novel concept in computational intelligence, opposition-based learning (OBL) was originally introduced to accelerate the population-based algorithm. In addition, its superiority has been proved mathematically and experimentally. Two key elements (function prototype and algorithm design) that influence the algorithm performance, however, has not yet fully discussed in the past decade. In this paper, two OBL strategies are reexamined in respect of function prototype and algorithm design. In the first part of this paper, considering the position relationship between the optimal solution and the center point, some well-known benchmark functions are divided into three categories. Then, quasi-opposition-based differential evolution (QODE) is investigated by two approaches: solving several benchmark functions of various function types and solving the same functions with a different optimal solution. The numerical experiments reveal that “smart” matching between the benchmark functions and the QOBL is an important factor to the good performance of QODE. In the second part of this paper, a novel individual-based embedding method is proposed to coincide with the classical definition of opposition-based optimization. Then, two opposition-based differential evolution algorithms are compared to discuss the differences between the two embedding methods. The experimental results confirm that the convergence differences stem from the embedding method chosen in the OBL scheme rather than the utilization rate of opposite points. Furthermore, the impacts caused by various function types and jumping rate are also discussed.

An Efficient Algorithm for Workflow Scheduling in the Clouds Based on Differential Evolution Method

The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers, storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on manimizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. The Cloud is comprised of computational and storage servers that aim to provision efficient access to remote and geographically distributed resources. To that end, many challenges, specifically workflow scheduling, are yet to be solved such. Despite it has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method, MODE. This algorithm not only ensures fast convergence but also averts getting trapped in local extrema. Our simulation experiments Cloud Sim show that MODE is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.

Thuật toán MODE giải bài toán lập lịch luồng công việc

Cloud computing is a new trend of information and communication technology that enables resource distribution and sharing at a large scale. The Cloud consists of a collection of virtual machine that promise to provision on-demand computational and storage resources when needed. End-users can access these resources via the Internet and have to pay only for their usage. Scheduling of scientific workflow applications on the Cloud is a challenging problem that has been the focus of many researchers for many years. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method. This algorithm does not only ensure fast convergence but it also averts getting trapped into local extrema. Our CloudSim-based simulations show that our algorithm is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.

Enhanced Opposition-based Differential Evolution Using Dynamic Optimum for Function Optimization

Opposition-based Differential Evolution (ODE) is one of the effective various DE variants. It is faster in convergence speed and robust in search abilities. The concept of opposition is utilized in the algorithm. During the algorithm, it evaluates an estimate and its corresponding opposite estimate at the same time. Many evaluation results on function optimization problems have shown the selection of the symmetry point between the estimate and the opposite estimate which will greatly influence variants’ performance. In this paper, we propose a new type of ODE variant, Oppositional Differential Evolution using the Dynamic Optimum (ODEDO). ODEDO uses the optimum in the current population to learn the opposite estimates, and employs a similar strategy as traditional ODE in population initialization and generation jumping. The approach is validated using 5 benchmark functions. The results show a better performance when compare ODEDO to classical DE and ODE.

A New Opposition-based Differential Evolution Variant Based on the Best and Worst Individuals

Considering the drawbacks of the traditional Opposition-based Differential Evolution (ODE), a new variant of ODE is presented. It makes full use of the information in the best and worst individuals in the current population to learn the opposite estimates, and follows the same framework of the traditional ODE in population initialization and generation jumping. The proposed approach is validated using the benchmark functions chosen from CEC 2015. Empirical results show that the proposed approach gains a better performance when compare to classical DE and ODE.

Dual problem of sorptive barrier design with a multiobjective approach

Sorptive barrier technology is a recently developed tool to separate hazardous contaminants from friendly environment. The design of sorptive barrier refers to configuring different amendments with sorptive ability of organic pollutant, which is an integer programming problem and a relatively time consuming problem as well. In this paper, sorptive barrier design is newly modeled in a biobjective optimization approach, in which the dual problem of sorptive barrier design is deduced. The objectives are to minimize the financial cost and the amount of pollutant leaking through barriers. Then an opposition-based adaptive multiobjective differential evolution algorithm (MODEA-OA) is applied to handle the proposed model. The Pareto optimal front obtained by MODEA-OA spreads accurately and evenly in all three instances tested. To select extreme optimal solutions, the original and dual sorptive barrier design problems can be solved simultaneously. This study suggests that modeling barrier design as a multiobjective optimization problem is an effective approach.

Opposition-based differential evolution for hydrothermal power system

This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Opposition-based differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed opposition-based differential evolution (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. The effectiveness of the proposed method has been verified on two test problems, two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed opposition-based differential evolution based approach is able to provide better solution.

A novel multi-objective memetic algorithm based on opposition-based self-adaptive differential evolution

Under the framework of evolutionary paradigms, many evolutionary algorithms have been designed for handling multi-objective optimization problems. Each of the different algorithms may display exceptionally good performance in certain optimization problems, but none of them can be completely superior over one another. As such, different evolutionary algorithms are being synthesized to complement each other in view of their strengths and the limitations inherent in them. In this study, the novel memetic algorithm known as the Opposition-based Self-adaptive Hybridized Differential Evolution algorithm (OSADE) is being comprehensively investigated through a comparative study with some state-of-the-art algorithms, such as NSGA-II, non-dominated sorting Differential Evolution (NSDE), MOEA/D-SBX, MOEA/D-DE and the Multi-objective Evolutionary Gradient Search (MO-EGS) by using a suite of different benchmark problems. Through the experimental results that are presented by employing the Inverted Generational Distance (IGD) and the Hausdorff Distance performance indicators, it is seen that OSADE is able to achieve competitive, if not better, performance when compared to the other algorithms in this study.