Hybrid Evolutionary Algorithm Research Articles

Dual-parameter controlled reconfigurable metasurface for enhanced terahertz beamforming via inverse design method

Recently, reconfigurable metasurfaces have emerged as a promising solution for wavefront manipulation in the terahertz (THz) region, providing enhanced beamforming capabilities. However, traditional single-parameter control methods fail to achieve independent phase and amplitude modulation, constraining their modulation capabilities. Meanwhile, forward design methods based on phase matching ignore the structural responses of the non-ideal unit, leading to degraded beamforming performance. Here, we introduce an electrically reconfigurable metasurface composed of bilayer graphene strips based on dual-parameter control. Full-wave simulations demonstrate independent amplitude and phase modulation, achieving the full 360° phase coverage and an adjustable amplitude range from 0 to 0.8 at 2.6 THz. To optimize beamforming performance, particularly for the responses of the non-ideal unit away from the designed frequency, we employed an inverse design method based on a hybrid evolutionary algorithm. This novel approach significantly enhances beam steering, achieving a maximum 60% increase in beam directivity and maintaining over 90% of ideal directivity across a broad frequency range from 1.6 THz to 5 THz. Especially, it achieves a maximum deflection angle of 75°. Meanwhile, the adaptability of the inverse design method is further demonstrated to various optimized objectives. For beam focusing, even with limited phase control (below 210°), this method significantly enhances the focusing quality (up to 150% enhancement) and increases the focusing efficiency from 25% to 40%. Additionally, it effectively mitigates the impact of quantized phase errors on beamforming. This research not only demonstrates potential applications in high-speed THz wireless communication and compact imaging systems but also paves the way for innovative designs in reconfigurable metasurfaces.

Strengthening energy transition through effective urban electric demand prediction: A multidisciplinary exploration of AI, policy, and administrative strategies

This research endeavors to strengthen energy transition in urban management through the enhanced accuracy of electric load demand forecasting within a multi-faceted approach considering legal and policy decision making associate with the proposed problem. Firstly, a prediction model is devised utilizing an evolving Peephole LSTM (PLSTM). This sophisticated architecture incorporates peephole connections, allowing for enhanced information flow within the network. Subsequently, a hybrid evolutionary algorithm is introduced for fine-tuning the parameters of the peephole LSTM (PLSTM). This hybrid approach amalgamates the whale optimization algorithm (WOA) and cultural algorithm (CA), harnessing their complementary strengths for optimal parameter configuration. The study extends beyond model development to address the practical implications of real-time electric load forecasting. A comprehensive stability analysis is conducted on datasets sourced from an urban area, offering insights into the model's reliability and performance under dynamic conditions. This tripartite investigation not only contributes to the advancement of predictive modeling in electric load demand forecasting but also underscores the significance of robust parameter optimization and stability assessments for real-world applicability in urban energy management. The simulation results on the practical dataset clearly show the high robustness and capabilities of the proposed model. By combining cutting-edge model architecture, hybrid evolutionary optimization, and thorough stability assessments, this study not only contributes to the forefront of predictive modeling in electric load forecasting but also emphasizes the critical importance of parameter optimization and stability evaluations for the seamless integration of such models into the dynamic landscape of urban energy management.

A hybrid evolutionary JAYA algorithm based on global optimization for 5G e-commerce logistics

With the increasing demand for logistics in modern society, how to achieve low-cost and efficient logistics delivery has become an urgent research topic. A hybrid evolutionary JAYA algorithm (H-JAYA) based on global optimization was designed to address the complex path planning problem of electric vehicles. This algorithm introduces a reverse learning mechanism to calculate the current optimal and worst individuals, while using differential perturbation mechanism and sine cosine operator to update the individual’s position. In addition, the study used the H-JAYA algorithm to construct a corresponding mathematical model for the optimization problem of electric vehicle paths. The results showed that in the three examples, the H-JAYA algorithm tested the optimal curve convergence speed, and it tended to stabilize after about 30 iterations. Meanwhile, in the RCDP5001 example, the total cost of the H-JAYA algorithm reached the lowest value of 623 yuan. The H-JAYA algorithm has significant advantages in solving the distribution path problem of electric vehicles, and can be well applied to practical logistics distribution, providing effective technical support for modern e-commerce logistics planning.

Thermal and mass exchange in a multiphase peristaltic flow with electric-debye-layer effects and chemical reactions using machine learning

The present investigation delves into the analysis of mass and thermal transmission during the peristalsis of a two phase flow of a Prandtl fluid, within a symmetric channel by considering entropy generation and minimization. The considered flow is examined by taking into account the impacts of electroosmotic effects through Electric Debye Layer (EDL) and chemical reactions followed by the lubrication theory approximation. Numerical solutions are acquired for the consequential differential equations through artificial neural networks (ANN) based fitness functions. The artificial neural networks (ANN) based fitness function optimized through hybrid evolutionary algorithms especially ABC (artificial bee colony) and NNA (neural network algorithm) like ANN-ABC-NNA. For the efficiency and validation of the algorithm we conduct the one hundred (100) independent runs. The obtained results through ANN-ABC-NNA are compared with the exact solutions for validation of results. In the present examination, the velocity profile is noticed to increase by the rise in the electroosmotic factor and solid particles concentration, while it decreases for the Prandtl fluid parameter. This research holds practical significance in diverse applications, particularly in the design and optimization of microfluidic devices.

Investigation and optimization of multiple objectives by hybrid evolutionary algorithms for turning of Nimonic 80A under nano MQL environment

ABSTRACT Nimonic80A is one of the hard-to-cut superalloy materials. However, machining the same remains difficult. The present study’s objective is attained by taking surface quality and tool wear as output core attributes of machining by deploying conventional statistical approaches and meta-heuristic optimisation algorithms to find optimal input attributes. Optimal input parameters were found using ANN hybridised with meta-heuristic optimisation algorithms, considering cutting acceleration as one of the response attributes along with cutting force (Fz), flank wear (Vb) and surface roughness (Ra). In this process, nanofluids were formulated by suspensions of graphene oxide into rice bran oil as the minimum quantity lubrication (MQL). Experiments were conducted based on L27 Taguchi experimental trials, and the experimental results revealed that minimum feed rate and cutting velocity reduced the cutting force, surface roughness, and tool flank wear up to 51% during nano-MQL turning conditions compared to dry machining. Thus, an artificial neural network (ANN) was used to generate the model for each machining response, which was further interfaced with multiobjective particle swarm optimisation (MOPSO) and multiobjective mayfly algorithms (MOMA) as a hybrid algorithm for finding optimal parameters. The optimal results were compared, and ANN-MOMA was found to be better.

A heuristic method for discovering multi-class classification rules from multi-source data in cloud–edge system

The integration of diverse devices has made the establishment of multi-class classification models for multi-source data a primary concern for data mining in cloud–edge system. Developing rule-based classifiers is essential because they present results that express the reasons for classification. However, existing rule learning methods are not compatible with multi-source tabular data containing mixed features, imbalanced labels and missing values, making it difficult to build cross-data source and cross-device data mining models. We developed a heuristic multi-class rule learning method that can handle complex tabular datasets without relying too much on cumbersome preprocessing techniques. We abstract the training process of the classifier into a multi-objective optimization problem and design a novel hybrid evolutionary algorithm to obtain Pareto-optimal solutions as rule-based classifier. Compared with the existing explainable classification methods, this method has obvious advantages in the classification performance of tabular data.

Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks

Distribution companies (DISCOs) aim to maximize their annual profits by performing the optimal planning of distributed generators (DGs) or energy storage systems (ESSs) in the deregulated electricity markets. Some previous studies have focused on the simultaneous planning of DGs and ESSs for DISCO profit maximization but have rarely considered the reactive powers of DGs and ESSs. In addition, the optimization methods used for solving this problem are either traditional or outdated, which may not yield superior results. To address these issues, this paper simultaneously performs the optimal planning of DGs and ESSs in distribution networks for DISCO profit maximization. The utilized model not only takes into account the revenues of trading active and reactive powers but also addresses the active and reactive powers of DGs and ESSs. To solve the optimization problem, a new hybrid evolutionary algorithm (EA) called the oppositional social engineering differential evolution with Lévy flights (OSEDE/LFs) is proposed. The OSEDE/LFs is applied to optimize the planning model using the 30-Bus and IEEE 69-Bus networks as test systems. The results of the two case studies are compared with several other EAs. The results confirm the significance of the planning model in achieving higher profits and demonstrate the effectiveness of the proposed approach when compared with other EAs.

Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms

Multi-Objective Security Constrained Unit Commitment via Hybrid Evolutionary Algorithms

Energy Efficient Hybrid Evolutionary Algorithm for Internet of Everything (IoE)-Enabled 6G

Energy Efficient Hybrid Evolutionary Algorithm for Internet of Everything (IoE)-Enabled 6G

A hierarchical surrogate assisted optimization algorithm using teaching-learning-based optimization and differential evolution for high-dimensional expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) are increasingly used in solving computationally expensive optimization problems. However, when tackling high-dimensional expensive problems, a large number of exact function evaluations (FEs) need to be consumed for existing SAEAs to achieve an acceptable solution. In this paper, a hierarchical surrogate assisted optimization algorithm (HSAOA) using teaching-learning-based optimization and differential evolution is proposed for solving high-dimensional expensive problems with a relatively small number of exact FEs. To keep a balance between global exploration and local exploitation, a hierarchical surrogate framework with hybrid evolutionary algorithms is devised. In the global search phase, a radial basis function surrogate is utilized to assist the teaching-learning-based optimization in locating the promising sub-regions. In the local search phase, a novel dynamic ensemble of surrogates is proposed to assist the differential evolution in speeding up the convergence process. Eight test functions with 10 to 100 dimensions and a spatial truss design problem are employed to compare the proposed method with several state-of-the-art SAEAs. The results show that the proposed HSAOA is superior to the comparison algorithms for solving expensive optimization problems, and needs a much smaller number of exact FEs than other competing SAEAs to produce competitive or even better results for high-dimensional expensive problems.

A multi-algorithm approach for operational human resources workload balancing in a last mile urban delivery system

Efficient workload assignment to the workforce is critical in last-mile package delivery systems. The explosive increase of e-commerce and last-mile package logistics after the COVID-19 pandemic has produced, among other issues, difficulties in balancing the daily workload of the workforce in many delivery zones. In this context, traditional methods of assigning package deliveries to workers based on geographical proximity can be inefficient and surely guide to an unbalanced workload distribution among delivery workers. In this paper, we look at the problem of operational human resources workload balancing in last-mile urban package delivery systems. The idea is to consider the effort workload to optimize the system, i.e., the optimization process is now focused on improving the delivery time, so that the workload balancing is complete among all the staff. This process should correct significant decompensations in workload among delivery workers in a given zone. Specifically, we propose a multi-algorithm approach to tackle this problem. The proposed approach takes as input a set of delivery points and a defined number of workers, and then assigns packages to workers, in such a way that it ensures that each worker completes a similar amount of work per day. The proposed algorithms use a combination of distance and workload considerations to optimize the allocation of packages to workers. In this sense, the distance between the delivery points and the location of each worker is also taken into account to minimize the travel time of the workers. The proposed multi-algorithm methodology includes different versions of k-means, evolutionary approaches, recursive assignments based on k-means initialization with different problem encodings, and a hybrid evolutionary ensemble algorithm. We have successfully illustrated the performance of the proposed approach in a real-world problem of human resource balancing in an urban last-mile package delivery workforce operating at Azuqueca de Henares, Guadalajara, Spain.

Heuristic Search for Rank Aggregation with Application to Label Ranking

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and an enhanced late acceptance local search method reinforced by a relaxed acceptance and replacement strategy and a fast incremental evaluation mechanism. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on both synthetic and real-world benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, a well-established machine learning task. We additionally analyze several key algorithmic components to gain insight into their operation. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72371157] and Shanghai Pujiang Programme [Grant 23PJC069]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0019 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0019 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numerical algorithm

In this research, we analyze the complex dynamics of hydro-magnetic flow and heat transport under Sorent and Dofour effects within wedge-shaped converging and diverging channels emphasizing its critical role in conventional system design, high-performance thermal equipment. We utilized artificial neural networks (ANNs) to investigation the dynamics of the problem. Our study centers on unraveling the intricacies of energy transport and entropy production arising from the pressure-driven flow of a non-Newtonian fluid within both convergent and divergent channel. The weights of ANN based fitness function ranging from − 10 to 10. To optimize the weights and biases of artificial neural networks (ANNs), employ a hybridization of advanced evolutionary optimization algorithms, specifically the artificial bee colony (ABC) optimization integrated with neural network algorithms (NNA). This approach allows us to identify and fine-tune the optimal weights within the neural network, enabling accurate prediction. We compare our results against the established different analytical and numerical methods to assess the effectiveness of our approach. The methodology undergoes a rigorous evaluation, encompassing multiple independent runs to ensure the robustness and reliability of our findings. Additionally, we conduct a comprehensive analysis that includes metrics such as mean squared error, minimum values, maximum values, average values, and standard deviation over these multiple independent runs. The minimum fitness function value is 1.32 × 10−8 computed across these multiple runs. The absolute error, between the HAM and machine learning approach addressed ranging from 3.55 × 10−7 to 1.90 × 10−8. This multifaceted evaluation ensures a thorough understanding of the performance and variability of our proposed approach, ultimately contributing to our understanding of entropy management in non-uniform channel flows, with valuable implications for diverse engineering applications.

Output-only structural damage identification based on Q-learning hybrid evolutionary algorithm and response reconstruction technique

Various structural damage identification methods have been developed and employed, while the absence of input excitation measurements may pose a huge challenge in their application since the input forces such as seismic load, traffic load, wind load, are not directly measurable. To address this issue, in the present paper, a novel output-only structural damage detection approach based on Q-learning hybrid evolutionary algorithm (QHEA) and response reconstruction technique is presented. On the one hand, the external excitation and structural acceleration responses are reconstructed with the aid of response reconstruction and Tikhonov regularization techniques in time domain. Structural damage identification can be formulated as an optimization-based inverse problem. On the other hand, a new optimization framework QHEA integrating Jaya algorithm, differential evolution, Q-learning algorithm is developed as search tool. The unknown structural parameters and unmeasured input force are iteratively updated by using two different measurement sets until the reconstructed acceleration responses agree well with the measured responses. Numerical examples involving a cantilever beam structure under single excitation and a simply-supported 51-bar truss structure under multiple excitations, as well as an experimental five-floor steel frame structure in the laboratory are carried out to validate the effectiveness of the proposed method. The final results demonstrate that the proposed method can accurately detect damage locations and quantify damage extents without the information of input excitation. In addition, the superiority of QHEA over other heuristic algorithms, the uncertainties of measurement noise and modeling errors on damage identification results are further examined.

Design of arbitrary energy distribution beam splitters base on multilayer metagratings by a hybrid evolutionary particle swarm optimization

Multilayer metagratings have strong wavefront manipulation capabilities and find important applications in beam splitters. Traditional methods rely on the phase gradient design of generalized Snell’s law, which can achieve highly efficient beam splitters with uniform energy distribution. However, designing arbitrary energy distributions in different channels under two orthogonal polarizations remains a challenge because it requires more complex structures to modulate the energy flow. In this work, we employed a hybrid evolutionary particle swarm optimization (HEPSO) from the combination of particle swarm optimization (PSO) and genetic algorithm (GA) which has a strong ability to find the optimal structures that satisfy the specific energy flow distributions. We used the crossover and mutation operators of GA to improve the global search capabilities, and the velocity updating formula of PSO to replace the selection operator of GA to avoid local optimization. Using this approach, we successfully designed a uniform beam splitter with an efficiency of over 90% and two beam splitters with arbitrary energy distributions, achieving an average error of about 0.5%. The optimal and average efficiencies obtained from running 10 optimizations are 2.2% and 4% higher than those obtained using PSO alone with 30 populations and 75 iterations. We envision that the proposed method can also provide an idea for other photonics design problems.

Enhanced decomposition-based hybrid evolutionary and gradient-based algorithm for many-objective optimization

Enhanced decomposition-based hybrid evolutionary and gradient-based algorithm for many-objective optimization

Parametric estimation of photovoltaic systems using a new multi-hybrid evolutionary algorithm

Integrating solar photovoltaic (PV) systems into the modern power grid introduces a variety of new problems. The accurate modelling of PV is required to strengthen the system characteristics in simulation environments. Modelling such PV systems is reflected by a nonlinear I–V characteristic curve behaviour with numerous unknown parameters because there is insufficient data in the cells’ datasheet. As a result, it is always a priority to identify these unknown parameters. To extract features of solar modules and build high-accuracy models for modelling, control, and optimization of PV systems, current–voltage data is required. A hybrid evolutionary algorithm is proposed in this paper for precise and effective parameter estimation from experimental data of various PV models. The proposed algorithm is named as hybrid flower grey differential (HFGD) algorithm and is based on the hybridization of flower pollination algorithm (FPA), grey wolf optimizer (GWO), and differential evolution (DE) algorithm. For performance evaluation, CEC 2019 benchmark data set is used. To increase the accuracy of the output solutions, we also combined the Newton–Raphson approach with the proposed algorithm. Four PV cells/modules with diverse characteristics, including RTC France Single Diode Model (SDM), RTC France Double DM (DDM), Amorphous Silicon aSi:H, and PVM 752 GaAs Thin-Film, are used to validate the effectiveness as well as the feasibility of the proposed algorithm. The parameter results obtained through the utilization of HFGD algorithm have been compared with other evolutionary algorithms through aspects of precision, reliability, and convergence. Based on the outcomes of the comparison, it has been seen that the HFGD algorithm obtained the lowest root-mean-square error (RMSE) value. Friedman’s rank and Wilcoxon test are carried out for the statistical analysis of the proposed work. The I–V and P–V characteristics are drawn along with the box plot for different PV cells/modules. Statistical and experimental results show the superiority of the proposed algorithm with respect to its counterpart.

An effective hybrid evolutionary algorithm for the set orienteering problem

The Set Orienteering Problem (SOP) is a variant of the popular Orienteering Problem (OP) arising from a number of real-life applications. The aim is to find a tour across a subset of customers, while maximizing the collected profit within a given travel time limit. In SOP, vertices (customers) are partitioned into clusters, where a profit is associated to each cluster. The profit of a cluster is collected only if at least one vertex belonging to the cluster is contained in the tour. For this NP-hard problem, we present a highly effective hybrid evolutionary algorithm that integrates a cluster-based crossover operator, a randomized mutation operator to generate multiple distinct promising offspring solutions, and a two-phase local refinement procedure that explores both feasible and infeasible solutions in search of high-quality local optima. Extensive experiments on 192 large benchmark instances show that the proposed algorithm significantly outperforms all the existing approaches from the SOP literature. In particular, it reports improved best-known solutions (new lower bounds) for 54 instances, while matching the existing best-known result for 133 instances. We further investigate the contribution of the key algorithmic elements to the success of the proposed approach.

Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios.

Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (Mpro) of SARS-CoV-2. We show that combinations of functional groups and sites are critical to design drugs with improved binding affinity, which can be easily achieved using our framework by exploring a fraction of the available search space. Atomistic simulations and experimental validation elucidate that enhanced and prolonged interactions between functionalized drugs and Mpro residues result in their improved therapeutic value over that of the parental compound. Overall, this novel framework is extremely flexible and has the potential to rapidly design inhibitors for any protein with available crystal structures.

A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production

During the car production process, diverse production workshops have distinct prerequisites for car body sequencing. This results in the intricate nature of sequencing within multi-stage car workshops. In this study, an optimization method for car body sequencing is proposed that combines a hybrid evolutionary algorithm with heuristic rules. In the welding workshop, a genetic algorithm is employed to optimize the vehicle sequencing. Simultaneously, a differential evolution algorithm is used to optimize the inbound sequence of the buffer zone between the welding and painting workshops, as well as the inbound sequence of the buffer zone between the painting and assembly workshops. Heuristic rules are applied to optimize the outbound sequence of the buffer zone between the welding and painting workshops, as well as the outbound sequence of the buffer zone between the painting and assembly workshops. In addition, in order to improve the quality of the initial population, a heuristic method-based initial population construction method is proposed. The optimization objectives are the number of vehicle model changes in the welding workshop, the number of color changes in the painting workshop, and the total number of overloads in the assembly workshop. The experimental results show that the proposed method performs better than the five outstanding evolutionary algorithms.