Adaptive Evolution Algorithm Research Articles

Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting

Global radiation is not a regularly measured parameter in any weather station relative to other meteorological parameters due to measurement costs. This study has proposed hybrid artificial neural network models that predicted monthly radiation using typical weather and geographic data. Two datasets and six artificial neural network models were respectively built for indigenous and widespread regions around the world. The referred models co-optimized the artificial neural network properties and feature selection. For this purpose, an adaptive evolutionary algorithm improving prediction performance was developed to train the neural networks. This novel approach has yielded promising results compared to the developed deep learning models in this study. The results revealed that while the indigenous models had common features of longitude, sunshine durations, precipitation, and wind speed, the widespread models involved those of latitude, sunshine durations, and mean daily maximum air temperature. The proposed hybrid model had respectively the best mean absolute percentage errors of 2.45% and 9.93% for validation dataset and 3.75% and 11.03% for testing dataset of the indigenous and widespread regions, respectively. The present findings showed that the proposed hybrid model could be evaluated as a generic model and could improve the forecasting accuracy with the specified optimization parameters.

Non-linear Domain Adaptation in Transfer Evolutionary Optimization

The cognitive ability to learn with experience is a hallmark of intelligent systems. The emerging transfer optimization paradigm pursues such human-like problem-solving prowess by leveraging useful information from various source tasks to enhance optimization efficiency on a related target task. The occurrence of harmful negative transfer is a key concern in this setting, paving the way for recent probabilistic model-based transfer evolutionary algorithms that curb this phenomenon. However, in applications where the source and target domains, i.e., the features of their respective search spaces (e.g., dimensionality) and the distribution of good solutions in those spaces, do not match, narrow focus on curbing negative effects can lead to the conservative cancellation of knowledge transfer. Taking this cue, this paper presents a novel perspective on domain adaptation in the context of evolutionary optimization, inducing positive transfers even in scenarios of source-target domain mismatch. Our first contribution is to establish a probabilistic formulation of domain adaptation, by which source and/or target tasks can be mapped to a common solution representation space in which their discrepancy is reduced. Secondly, a domain adaptive transfer evolutionary algorithm is proposed, supporting both offline construction and online data-driven learning of non-linear mapping functions. The performance of the algorithm is experimentally verified, demonstrating superior convergence rate in comparison to state-of-the-art baselines on synthetic benchmarks and a practical case study in multi-location inventory planning. Our results thus shed light on a new research direction for optimization algorithms that improve their efficacy by learning from heterogeneous experiential priors.

Integrated optimization method for helical gear hobbing parameters considering machining efficiency, cost and precision

The formulation of dry hobbing processing parameters depends heavily on mass experiments and the experiences of skilled technicians, and the use of unsuitable parameters will lead to high cost, low efficiency and severe precision defects. To resolve this issue, a helical gear processing parameter optimization method (PPOM-HG) is proposed in this paper. First, an efficiency-cost-accuracy triple-target optimization model is established. A manufacturing efficiency model is established through a detailed analysis of the geometric trajectory of a hob. A helical gear manufacturing cost model is established by analyzing the power curve of the hobbing machine and the hob’s lifetimes under various processing parameters. A modified correlation analysis random forest (CARF) model is designed for predicting gear machining precision, which replaces the traditional empirical precision function. Then, for searching for the optimal solution of the established efficiency-cost-precision triple-target model, an adaptive multiobjective fusion evolutionary algorithm (AMFEA) with adaptive evolution parameters is proposed. Finally, via many helical gear machining experiments, the validity and advantages of the proposed CARF and AMFEA methods are demonstrated, and the selection strategy of the Pareto front solution under various conditions is discussed.

Adaptive ε-Constraint Multi-Objective Evolutionary Algorithm Based on Decomposition and Differential Evolution

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents an adaptive $\varepsilon $ -constraint multi-objective evolutionary algorithm based on decomposition and differential evolution ( $\varepsilon $ -MOEA/D-DE). First, an adaptive $\varepsilon $ -constraint strategy based on both evolution generation and constraint violation is designed to make better use of excellent evolution individuals and improve population diversity. Then, an adaptive differential evolution (DE) mutation strategy with full utilization of infeasible individuals is proposed to increase search efficiency and avoid falling into the local optimum. Finally, a replacement mechanism is suggested to take advantage of the infeasible individuals in the population with better objective function values and constraint violation degree, and thus both diversity and convergence are well coordinated. A comparative experiment with four other excellent constrained multi-objective algorithms was implemented on standard constrained multi-objective optimization problems (CF series), and the results showed that the diversity and convergence of our algorithm were both improved.

An Elite Adaptive Niche Evolutionary Algorithm for Duty Clustering Problem in SoWSN

The recent success of emerging low power wireless sensor networks technology has encouraged researchers to create novel duty cycle design algorithm in this area. Since “sensors are constrained in sensing capabilities”, duty cycle design plays a crucial role in maximizing the point coverage rate, while most researches for duty cycle design are related to a duty cycle design algorithm. But unfortunately, the ideal duty cycle design requires an exhaustive search over all combinations of the allowed combinations. In this letter, we present a new elite adaptive niche evolutionary algorithm (EANEA) for duty cycle design problem in Service-oriented wireless sensor networks (SoWSN). In order to extend the network life cycle, we designed an objective function for SoWSN. We also give an EANEA which, depending on a powerful niche operator, blends the merits of both elite selection and adaptive adjusting for the channel assignment problem. Simulation results show that the shown algorithm can achieve a higher point coverage rate over genetic quantum algorithm (QGA) and Shuffled Frog-Leaping Algorithm (SFLA). Moreover, the optimization employs an elite selection to initialize the parameters and avoid local optima proficiently.

An adaptive decomposition evolutionary algorithm based on environmental information for many-objective optimization

The performance of traditional penalty boundary intersection (PBI) decomposition-based evolutionary algorithm is totally determined by the penalty factor. The fixed penalty factor causes the imbalance between the convergence and the diversity when solving many-objective problems. So, an adaptive decomposition evolutionary algorithm based on environmental information (MaOEA/ADEI) is proposed to solve the imbalance. The penalty factor of PBI decomposition is determined by the environmental information (include distribution information of weight vectors and population). Furthermore, the parent individual selection strategy is introduced to select promising individuals for variation and the weight vectors adaption strategy is used to handle problems with scaled objectives. Comparisons with 4 algorithms on 24 benchmark instances are used to test the property of MaOEA/ADEI. The experimental results show MaOEA/ADEI performs best on 14 test instances.

An Adaptive Reference Vector-Guided Evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems.

Most reference vector-based decomposition algorithms for solving multiobjective optimization problems may not be well suited for solving problems with irregular Pareto fronts (PFs) because the distribution of predefined reference vectors may not match well with the distribution of the Pareto-optimal solutions. Thus, the adaptation of the reference vectors is an intuitive way for decomposition-based algorithms to deal with irregular PFs. However, most existing methods frequently change the reference vectors based on the activeness of the reference vectors within specific generations, slowing down the convergence of the search process. To address this issue, we propose a new method to learn the distribution of the reference vectors using the growing neural gas (GNG) network to achieve automatic yet stable adaptation. To this end, an improved GNG is designed for learning the topology of the PFs with the solutions generated during a period of the search process as the training data. We use the individuals in the current population as well as those in previous generations to train the GNG to strike a balance between exploration and exploitation. Comparative studies conducted on popular benchmark problems and a real-world hybrid vehicle controller design problem with complex and irregular PFs show that the proposed method is very competitive.

Hybrid Microgrid Many-Objective Sizing Optimization With Fuzzy Decision

The economics, reliability, and carbon efficiency of hybrid microgrid systems (HMSs) are often in conflict; hence, a reasonable design for the sizing of the initial microgrid is important. In this article, we propose an improved two-archive many-objective evolutionary algorithm (TA-MaEA) based on fuzzy decision to solve the sizing optimization problem for HMSs. For the HMS simulated in this article, costs, loss of power supply probability, pollutant emissions, and power balance are considered as objective functions. For the proposed algorithm, we employ two archives with different diversity selection strategies to balance convergence and diversity in the high-dimensional objective space. In addition, a fuzzy decision making method is proposed to further help decision makers obtain a solution from the Pareto front that optimally balances the objectives. The effectiveness of the proposed algorithm in solving the HMS sizing optimization problem is investigated for the case of Yanbu, Saudi Arabia. The experimental results show that, compared with the two-archive evolutionary algorithm for constrained many-objective optimization (C-TAEA), the clustering-based adaptive many-objective evolutionary algorithm (CA-MOEA), and the improved decomposition-based evolutionary algorithm (I-DBEA), the proposed algorithm can reduce the system costs by 7%, 13%, and 21%, respectively.

Multi-objective energy management of multiple microgrids under random electric vehicle charging

In view of the increasing development of decentralized power systems and electric vehicles, this paper seeks to improve the energy management performance of multiple microgrid systems under the uncertainty associated with electric vehicle charging. A multi-objective optimization model is established for minimizing the transmission losses, operating costs, and carbon emissions of multiple microgrid systems. Firstly, a novel method is proposed for forecasting electric vehicle charging loads based on a back propagation neural network improved by long short-term memory deep learning. Based on the forecast data, a double layer solution algorithm is proposed, which consists of an adaptive multi-objective evolutionary algorithm based on decomposition and differential evolution at the multiple microgrids layer and a modified consistency algorithm for fast economic scheduling at the single microgrid layer. Finally, a model system composed of four interconnected IEEE microgrids is simulated as a case study, and the performance of the proposed algorithm is compared with that of conventional multi-objective evolutionary algorithms based on decomposition. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed improved algorithm.

Dynamic equivalent method of PMSG‐based wind farm for power system stability analysis

This study presents a dynamic equivalent method of wind farms with permanent magnetic synchronous generator (PMSG) wind turbines for power system stability analysis. First, a novel clustering indicator is set up based on the morphology similarity distance and cosine similarity between trajectories with the measurable output characteristics time series of wind turbines, and the K-means algorithm is applied to cluster the PMSGs. Combining silhouette coefficient, the robustness analysis of the clustering method under different scenarios is adopted. In each cluster, a trajectory sensitivity based key parameters selection is introduced to reduce the control parameters to be identified. Moreover, a multi-objective adaptive function is established and the multi-controllers aggregated parameters are optimised by adaptive evolutionary particle swarm optimisation algorithm. Thus, a wind farm model represented by multi equivalent wind turbines is obtained. The IEEE 39-bus system with a PMSG-based wind farm is used as test system to verify the validity of the proposed dynamic equivalent approach in time domain and frequency domain, respectively. It has been demonstrated that the proposed model can reduce the model complexity and improve the calculation efficiency of wind farms.

Visual Control Law Tuning Using the JADE Algorithm Applied to Leaf Detection and Cutting

In the propagation of plant tissue in vitro it is necessary to obtain from the leaf a circular section cut. In order to achieve this type of cuts the visual feedback is used, allowing us to analyze the RGB planes to detect the green color and find the image centroid. In order to fulfill the cut objective a PD+ control is used with self-tuning based on the adaptive differential evolutionary algorithm JADE.

Uniform distribution driven adaptive differential evolution

Evolutionary algorithms are popular optimization tools for real-world applications due to their numerous advantages such as capability of parallel search along multiple directions by maintaining a population of candidates, invariance to certain mathematical properties (convexity, continuity and hardness) of fitness landscape and ability to handle black-box problems. However, most of the current evolutionary algorithms are loosely based on heuristics inspired by nature and lack the crucial theoretical background. Motivated by the overwhelming advantages of such optimization algorithms and the necessity for theoretical foundation, this paper presents a new evolutionary algorithm - UDE (Uniform Differential Evolution) for solving single- objective optimization problems along with a theoretical analysis of the proposed UDE algorithm. Thus, this paper formally gives insights about the features and properties of the various optimization strategies used. This method is different from traditional Differential Evolution variants as it employs a uniform probability distribution for generating new candidate solutions. UDE is further developed to obtain an adaptive evolutionary algorithm - Adaptive UDE (AUDE), which has shown to obtain significant improvements in the performance and convergence speeds compared to other algorithms on a benchmark set of 19 test problems. The source codes are available at http://worksupplements.droppages.com/ude_aude .

Elite Adaptive Parallel Evolutionary Algorithm for Sensor Nodes Clustering Optimization in High Density Sensor Network

Minimizing the communication energy consumption has always been a key aspect of the research of High Density Sensor Network (HDSN). HDSN is a group of small devices with restricted battery capacities. Accordingly, the objective of the clustering technique is to design a best set of sensor nodes clustering result for the combination that minimize the communication energy consumption. The ideal clustering problem with discrete variables is a np-hard problem in its accurate formulation. In this study, an improved elite adaptive parallel evolutionary algorithm (EAPEA) is proposed to lower the communication energy consumption in HDSN. The suggested algorithm is tested by a fitness function, and new adaptive operator and elite operator have been designed to speed up the convergence rate. The EAPEA merges the merits of the adaptive adjusting and elite selection. By the mechanism of adaptive adjusting, its overall exploit capacity is fully applied for finding the overall optimal solution. EAPEA simultaneously develop a large number of results to explore the explore area and to avoid local optima. A convergence analysis is carried out, and extensive experiments are carried out that compare EAPEA with two other heuristics. Our experimental simulations denote that the represented EAPEA technique, when used into HDSN, is competent to offer better performance and a low communication energy consumption compared to two other heuristics.

An adaptive clustering-based evolutionary algorithm for many-objective optimization problems

This paper proposes an adaptive clustering-based evolutionary algorithm for many-objective optimization problems (MaOPs), called MaOEA/AC. In this algorithm, an adaptive clustering strategy (ACS) is first introduced to divide the population into multiple clusters, which can properly fit various Pareto fronts (PFs) of the target MaOPs. Then, the environmental selection of MaOEA/AC is designed based on these clusters to collect the solutions with balanceable convergence and diversity. To be more detail, the similarity between solutions in ACS is appropriately measured by computing the Euclidean distance between their projections on an adaptive unit hyper-surface, whose curving rate is controlled by a parameter p. A simple yet effective estimation method is proposed to get a suitable value of p based on the distribution of the current non-dominated solution set, so that the estimated unit hyper-surface can roughly reflect the characteristics of PFs in the target MaOPs. The effectiveness of MaOEA/AC is validated by numerous experimental studies on solving test MaOPs with various PFs, which have the characteristics with convex, concave, inverted, disconnected, degenerated, and other mixed or irregular PFs. The experiments also show that MaOEA/AC has the superior performance over several recent many-objective evolutionary algorithms, when solving most of these test MaOPs.

Concurrent optimization of multiple base learners in neural network ensembles: An adaptive niching differential evolution approach

Neural network ensemble (NNE) exhibits improved performance when compared with a single neural network (NN) in most cases. Traditionally, each base network in an NNE is trained individually, which may result in network redundancy and expensive training overhead. This paper proposes a new adaptive niching evolutionary algorithm, which possesses promising performance in finding multiple optima in terms of good accuracy and diversity. By means of this algorithm, all NNs in an NNE can be trained simultaneously. In particular, the proposed algorithm is named adaptive niching differential evolution (ANDE), which is characterized by a heuristic clustering method to enable iteratively cluster subpopulations that track and locate multiple optima, a parameter adaptation strategy to adaptively adjust parameters according to the subpopulation states, and an auxiliary movement scheme to promote the equilibrium between exploration and exploitation. Experimental results validate the efficiency and effectiveness of the proposed ANDE on the benchmark test suite of multimodal optimization. Furthermore, ANDE is extended to concurrently train multiple base NNs for ensemble and the experiments show a promising performance of ANDE-NNE.

Computational optimization for porosity-dependent isogeometric analysis of functionally graded sandwich nanoplates

A simply and effectively computational optimization for porosity-dependent isogeometric analysis of functionally graded (FG) sandwich nanoplates is proposed for the first time. Porosity-dependent material properties are defined via the modified power law function. The distribution of ceramic volume fraction is approximated by using the multi-patch B-spline basis functions through the thickness direction. This approach ensures smoothly and continuously vary material properties across each layer, and automatically satisfies the C0-continuity at each layer interfaces. To consider length scale effects, the Eringen’s nonlocal elasticity theory is used to model porous FG sandwich nanoplates. Based on a combination of NURBS formulations and four variables refined plate theory, governing equations of the nanoplates are derived and employed to obtain natural frequencies of the porous FG sandwich nanoplates. The present approximation is easy to satisfy the requirement of at least third order derivatives of basis functions in approximate formulations of nanoplates. To save computational costs, an adaptive hybrid evolutionary firefly algorithm is used. Continuous design variables including the thickness of each layer and the ceramic volume fraction at control points are considered for constraint optimization problems. New results are performed and considered as benchmark results for further studies on the porous FG sandwich nanoplates.

A hybrid-encoding adaptive evolutionary strategy algorithm for windage alteration fault diagnosis

It is critically important that windage alteration faults (WAFs) within mine ventilation systems be quickly identified and mitigated in order to ensure a safe mine production environment. Thus, we propose a Hybrid-Encoding adaptive Evolution Strategy (ES) Algorithm to diagnose the fault’s location and volume quickly and accurately, as it combines classification and regression features. The Euclidean distance between the airflow set calculated via fault diagnosis and the airflow set obtained by the monitoring system was used as the objective function value. Six benchmark functions and one thousand six hundred tests were carried out to verify the feasibility of using Hybrid-Encoding for WAFs diagnosis. The effectiveness of adaptive ES was demonstrated by Genetic Algorithm (GA), Differential Evolution Algorithm (DEA), and Particle Swarm Optimization (PSO). The experimental results fully validate the Hybrid-Encoding adaptive ES superiority in terms of accuracy, precision, diagnostic errors, robustness, computational efficiency, and convergence speed, etc. Diagnostic accuracy and precision during field testing were both 92.5 %, and 93.75 % of the results showed relative errors of < 5 %. Thus, our proposed Hybrid-Encoding adaptive ES Algorithm meets the requirements for fault diagnosis accuracy at the mine production site.

A Two-Stage Evolutionary Fuzzy Clustering Framework for Noisy Image Segmentation

This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization stage, the fuzzy clustering on pixels in inhomogeneous regions is converted into a multi-objective problem, which can preserve image details while restraining noise. The multi-objective problem is decomposed into several sub-problems by the Tchebycheff approach. A trade-off can be obtained by optimizing these sub-problems simultaneously. In the fuzzy clustering segmentation stage, fuzzy clustering with the trade-off between preserving image details and restraining noise is performed on the whole observed image. To deal with this fuzzy clustering problem, an adaptive evolutionary fuzzy clustering algorithm with spatial information is proposed. Experiment results on synthetic and real images illustrate the effectiveness of the proposed framework for noisy image segmentation.

An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling

An Adaptive Multi-Objective Evolutionary Algorithm with Two-Stage Local Search for Flexible Job-Shop Scheduling

Optimal design of FG sandwich nanoplates using size-dependent isogeometric analysis

In this study, an effectively computational optimization approach for size-dependent isogeometric analysis of functionally graded (FG) sandwich nanoplates is proposed. The multi-patch B-spline basis functions through the thickness direction are employed to approximate distributions of ceramic volume fraction. This approach smoothly and continuously ensures the distributions of ceramic volume fraction across each layer, and automatically satisfies the C0-continuity at each layer interface. The material properties based on both Mori–Tanaka scheme and the rule of mixture are implemented. To capture the size effects, the Eringen's nonlocal elasticity is used. Governing equations are then derived and a combination of the NURBS formulation and four variables refined plate theory (RPT) are employed to analyze natural frequencies of the FG sandwich nanoplates. Thanks to continuous higher-order derivatives of NURBS basis functions, the present approximation is easy to satisfy the requirement of C2-continuity of the FG sandwich nanoplates. An adaptive hybrid evolutionary firefly algorithm with fast convergence speed and less computational cost is used as an optimizer. The ceramic volume fraction across the z-direction at control points and the thickness of each layer are considered as continuous design variables. Numerical examples for free vibration and optimization analyses of the FG sandwich nanoplates are examined. Several new results are obtained and considered as benchmark problems for further studies on the FG sandwich nanoplates.