Hybrid Evolutionary Algorithm Research Articles

Overbounding the Model Uncertainty for Kalman Filter-Based Advanced Receiver Autonomous Integrity Monitoring in the Presence of Time Correlation by the Hybrid Evolutionary Algorithm

Overbounding the Model Uncertainty for Kalman Filter-Based Advanced Receiver Autonomous Integrity Monitoring in the Presence of Time Correlation by the Hybrid Evolutionary Algorithm

Hybrid evolutionary algorithms (hybrid-GAPSO) based optimization of MIG welding process

MIG welding is a modern part of advanced welding process. In this article, the mathematical modeling by ANN and parametric optimization of MIG welding joining process of cast iron has been propounded using soft computing hybrid-GAPSO. The article also shows the best model using NN basis of fittest with experimental result, computational time, convergence speed and finally RMSE obtained from cross-validation. In this research, the optimal value of the input variables obtained by hybrid-HGAPSO so that output variables hardness (HA) will be maximum & surface roughness (SR) and width of breadth thickness (WOBT) will be minimum, has been illustrated. In this experimental analysis minimized RMSE of 1.15%, 1.433%, and 1.6678% for HA, WOBT and SR are accomplished respectively by HGAPSO-ANN.

A hybrid evolutionary algorithm integrated with specific neighborhood search for collaborative task planning of heterogeneous multi-UAVs under SEAD scenarios

A hybrid evolutionary algorithm integrated with specific neighborhood search for collaborative task planning of heterogeneous multi-UAVs under SEAD scenarios

Multi-Objective Hybrid Algorithm Integrating Gradient Search and Evolutionary Mechanisms

The current multi-objective evolutionary algorithm (MOEA) has attracted much attention because of its good global exploration ability, but its local search ability near the optimal value is relatively weak, and for optimization prob lems with large-scale decision variables, the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient-based optimization algorithms can overcome these problems well, but they are difficult to be applied to multi-objective problems (MOPs). Therefore, this paper introduced random weight function on the basis of weighted average gradient, developed multi-objective gradient operator, and combined it with non-dominated genetic algorithm based on reference points (NSGA- III) proposed by Deb in 2013 to develop multi-objective optimization algorithm (MOGBA) and multi-objective Hybrid Evolutionary algorithm (HMOEA). The latter greatly enhances the local search capability while retaining the good global exploration capability of NSGA-III. Numerical experiments show that HMOEA has excellent capture capability for various Pareto formations, and the efficiency is improved by times compared with typical multi-objective algorithms. And further HMOEA is applied to the multi-objective aerodynamic optimization problem of the RAE2822 airfoil, and the ideal Pareto front is obtained, indicating that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm

Reasonable deployment of temperature sensors is the key to accurately monitoring the temperature field of machine tools and improving the accuracy of thermal error prediction and compensation models. To determine the optimal deployment location of sensors, this paper proposes a temperature-sensitive points selection method tightly coupled with rough set and multi-objective optimization. Firstly, the importance of each temperature measurement point to the thermal error is calculated based on the rough set, and information entropy is introduced to amplify the importance difference among adjacent measurement points at the same heat source. Then, with the temperature measurement points groups as the variables, the number of temperature measurement points in the group, and the information importance of the group as the objectives, a multi-objective attribute reduction model is established, which transforms the temperature-sensitive points selection problem into a discrete multi-objective optimization problem. Finally, a multi-objective adaptive hybrid evolutionary algorithm is proposed, which designs a population initialization method based on mutual information and interval probability, and dynamic adaptive evolutionary parameters to achieve optimal temperature-sensitive points selection. Experiments on the high-speed dry hobbing machine verify the superiority and effectiveness of the proposed method.

Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies

In the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching.

Hybrid differential evolution algorithm for Falkner-Skan flow with rotation

A hybrid differential evolution algorithm is used in this work to study the rotating transport of Falkner-Skan flow. The problem is modeled as an equivalent optimization problem by using Padé rational approximation functions. The primary model of the governing partial differential equations is imposed to subjugate the error between profiles. A hybrid evolutionary algorithm based on differential evolution and a convergent version of Nelder-Mead direct search algorithm is employed to perform global exploratory search along with an enhanced exploitation to improve the accuracy of the proposed solution scheme. The resulting scheme is named as evolutionary Padé approximation (EPA) scheme. The performance of the proposed EPA scheme on the Falkner-Skan boundary value problem is investigated by considering various values of the rotation parameters. The developed optimizer in EPA scheme was able to minimize the residuals up to10−10. Results are displayed graphically in order to study the effect of various types of parameters. EPA scheme determined that angular velocity increases or decreases accordingly as fluid parameter (β) and the rotation parameter (λ) but shows inverse behavior with respect to power law index(n). Similarly, the response of velocity profile along y−axis was decreasing function of β as well as n but increasing function of λ. The performance of the proposed EPA scheme has been demonstrated by comparing results with a hybrid neural network scheme and found in excellent agreement.

Retraction Note: Hybrid evolutionary algorithm (NNACOR) for energy minimization in a wireless mesh topology towards green computing

Retraction Note: Hybrid evolutionary algorithm (NNACOR) for energy minimization in a wireless mesh topology towards green computing

Improving quality of service for Internet of Things(IoT) in real life application: A novel adaptation based Hybrid Evolutionary Algorithm

In this paper, we address critical challenges in IoT sensor lifespan, service latency, and coverage area, all impacting energy consumption in smart agriculture applications. To enhance the quality of service (QoS) while prolonging the energy efficiency of smart sensors, a novel optimization algorithm is introduced. Referred to as the ”Adaptation-Based Hybrid Evolutionary Algorithm,” this innovative approach combines the strengths of Grey Wolf Optimizers (GWO) and Differential Evolution (DE) algorithms. The methodology involves a new adaptation-based strategy and incorporates a hybrid algorithm that synergizes the exploratory and exploitative capabilities of both GWO and DE algorithms. This hybrid approach is leveraged to meticulously select optimal mutation new adaptation services, drawing from the GWO and DE algorithm frameworks. Notably, the algorithm’s control parameters autonomously adjust through insights gained from prior evolutionary searches. Furthermore, we enhance the DE-based crossover technique by integrating the proficient search capabilities of the GWO algorithm, renowned for tackling continuous global optimization problems. To validate our approach, we apply it to IoT scenarios and optimize QoS through a fitness function that comprehensively accounts for energy consumption, coverage rate, lifespan, and latency. Comparative evaluations against standard algorithms underscore the superior performance of our proposed methodology, particularly evident in its application to IoT-smart agriculture settings.

An output-only damage identification method based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction

The absence of excitation measurements may pose a huge challenge in the application of many damage identification methods since it is difficult to acquire the external excitations, such as wind load, traffic load. To deal with this issue, a novel output-only structural damage identification approach based on reinforcement-aided evolutionary algorithm and heterogeneous response reconstruction with Bayesian inference regularization is developed. On the one hand, heterogeneous measurements (e.g., displacements, strains, accelerations) are rescaled and reconstructed with the aid of Bayesian inference regularization technique. Structural damages are identified by minimizing the discrepancies between the measured and reconstructed responses. On the other hand, to solve the optimization-based inverse problem, a reinforcement-aided evolutionary algorithm, named Q-learning hybrid evolutionary algorithm (QHEA), integrating Jaya algorithm, differential algorithm, and Q-learning algorithm is proposed as search tool. To validate the feasibility and applicability of the proposed method, numerical studies on a three-span beam structure and laboratory tests on a five-story steel frame structure are carried out. The effects of data rescaling and data fusion on response reconstruction and damage identification are also investigated. The results clearly demonstrate the superiority of QHEA over other heuristic algorithms and heterogeneous data fusion over a single type of measurement. It is shown that both the locations and extents of the damaged elements can be accurately identified by the proposed method without the information of input force.

A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem

The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this computationally challenging problem, we propose a reinforcement learning guided hybrid evolutionary algorithm following the framework of the memetic algorithm. The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood descent to determine the exploration order of multiple neighborhoods. Additionally, strategic oscillation is used to achieve a balanced exploration of both feasible and infeasible solutions. The competitiveness of the algorithm against state-of-the-art methods is demonstrated by experimental results on the three sets of 76 popular instances, including 51 improved best solutions (new upper bounds) for the 59 instances with unknown optima and equal best results for the remaining instances. We also conduct additional experiments to shed light on the key components of the algorithm.

Adaptive control of transportation infrastructure in an urban environment using a real-coded genetic algorithm

The management of urban areas requires the development of an effective strategy for the evolution of transportation infrastructure to ensure the smooth flow of traffic and pedestrians. A crucial component of this infrastructure is the traffic light system, which plays a vital role in traffic control and traffic safety. Improving the efficiency of traffic control systems in intelligent transportation systems (ITS) has a significant impact on a city’s economy. As a result, the cost of fuel for road users can be reduced, and their level of social comfort can be improved, among other benefits. This paper proposes a novel approach to optimizing traffic flows in smart cities, based on the combined use of the genetic optimization algorithm and the ITS simulation model we developed. The proposed method aims to enhance the efficiency of existing traffic control systems and achieve optimal traffic flow patterns, thereby contributing to a more sustainable and efficient urban environment. The optimization algorithm shown here aggregates the objective functions using a simulation model of a real region of the Moscow road network. The model includes intersections, pedestrian crossings and other features that are implemented in the AnyLogic system. The research aims to create a decision-support system for managing urban transport infrastructure. This system will be used to optimize the duration of traffic light phases in order to minimize the time vehicles spend passing through key nodes in the urban road network. It will also optimize pedestrian flow, reducing the impact of traffic on the environment and improving fuel efficiency. By applying this approach, the capacity of the street network can be significantly increased. Additionally, the negative effects of traffic flow on the environment can be reduced by optimizing fuel use and reducing waiting times at intersections managed by traffic lights. The research methodology involves the development of a hybrid evolutionary search algorithm, the creation of a simulation model for transportation and pedestrian flows in the AnyLogic and a series of optimization experiments that demonstrate the effectiveness of the proposed approach when applied to the modeling of complex urban transportation systems.

Quality of Service Based on Scheduling in Cloud/Fog Environment

In the area of fog and cloud computing, efficient work scheduling is critical for optimising resource allocation and raising Quality of Service (QOS) levels. Research in this area has resulted in a variety of ways for dealing with this problem. One line of research focuses on lowering data latency and improving QOS in fog-cloud systems by precise job scheduling. This requires intelligently dividing jobs across fog and cloud resources to reduce data processing and delivery delays, ultimately improving the overall user experience. Furthermore, the investigation encompasses novel methodologies such as hybrid evolutionary algorithms and heuristic strategies. These approaches seek to create a compromise between competing objectives, such as cost and energy efficiency, while also achieving QOS criteria. Researchers aim to develop scheduling systems that optimise resource utilisation and reduce operating costs without sacrificing service quality by applying evolutionary concepts or combining heuristic methods. Furthermore, ongoing discussions focus on improving resource scheduling approaches to achieve optimal QOS results. This entails continual evaluation and adaption of scheduling algorithms to changing environmental conditions, workload fluctuations, and user requests. Furthermore, developing methodologies such as the CODA approach provide intriguing avenues for efficient job scheduling in fog computing settings, with the potential to revolutionise future resource allocation and QOS enhancement efforts.

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.

Mixed Integer Nonlinear Programming-Based Unit Commitment of Conventional Thermal Generators Using Hybrid Evolutionary Algorithms

Unit Commitment (UC) discusses the optimized generation resources (to turn on economical generators and turn off expensive generators),which are subjected to satisfy all the operational constraints. The operational constraints such as load balancing, security maximization, minimum up and down time, spinning reserve, and ramp up and down constraints are difficult to satisfy. Although, UC is a cost minimization problem that is realized by committing less expensive units while satisfying the corresponding constraints, and dispatching the committed units economically. The UC problem is an np-hard Mixed Integer Nonlinear Problem (MINLP). Therefore, in this paper, hybrid EA based on a Genetic Algorithm (GA) has been applied to find the optimal solution to the UC problem. Moreover, during the search process, it is very difficult to discard infeasible solutions in EAs. Hence, the Genetic Algorithm (GA) is integrated with the feasibility rule constraint handling technique to emphasize feasible solutions. IEEE RTS Eleven Thermal Generator Standard Test system is used to validate the performance of proposed methods. For the validation and the superiority of the proposed algorithm, simulation results are compared with the classical Lagrangian Relaxation (LR) methods. Results show that the proposed method can find the global optimal solution to the UC problem which is subjected to satisfy all the operational constraints.

Enhancing On-demand Transportation: Prioritizing Travel Costs with Hybrid Evolutionary Algorithms

Objective: Enhancing sustainability in on-demand transportation is a challenging task by offering flexible and optimized mobility solutions. This study focuses on on-demand mobility systems. We introduce a novel evolutionary computing method designed to address a bi-objective customized on-demand transportation problem, aiming to minimize total travel cost and total waiting time. Methods: The method incorporates specific optimization techniques, along with an efficient dominance sorting approach, using an intelligent candidate list to reduce computational time. Results: Comparative results demonstrate the effectiveness of this hybrid computing method, particularly when prioritizing total travel cost from the service provider’s perspective. Conclusion: These findings present a promising framework for decision-makers, empowering them to navigate favorable compromises between conflicting objectives and make informed choices aligned with their preferences and customer-oriented constraints.

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.