Hybrid Genetic Algorithm Research Articles

A memetic algorithm for the flexible periodic vehicle routing problem

A memetic algorithm for the flexible periodic vehicle routing problem

An effective feature selection approach based on hybrid Grey Wolf Optimizer and Genetic Algorithm for hyperspectral image

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains an NP-hard challenge, as existing swarm intelligence and evolutionary algorithms (SIEAs) often suffer from limited exploration capabilities or susceptibility to local optima, particularly in high-dimensional scenarios. To address these challenges, we propose GWOGA, a novel hybrid algorithm that combines Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA), aiming to achieve an effective balance between exploration and exploitation. The innovation of GWOGA lies in three core strategies: (1) chaotic map and Opposition-Based Learning (OBL) for uniformly distributed population initialization, enhancing diversity and mitigating premature convergence; (2) elite learning strategy to prioritize high-ranking solutions, strengthening the search hierarchy and efficiency; and (3) a hybrid optimization mechanism where GWO ensures rapid early-stage convergence, while GA refines global search in later stages to escape local optima. Experiments on three benchmark HSIs (i.e., Indian Pines, KSC, and Botswana) demonstrate that GWOGA outperforms state-of-the-art algorithms, achieving higher classification accuracy with fewer selected bands. The results highlight GWOGA’s robustness, generalizability, and potential for real-world applications in HSI FS.

Optimal Task Allocation and Sequencing for Flight Test Based on a Memetic Algorithm With Lexicographic Optimisation

ABSTRACTThe flight test plays an important role in the development of an aircraft. Currently, with the increasing complexity and higher validation requirements for aircraft, there is a crucial need to generate high‐quality flight test task schedules in an efficient way. This paper proposes a flight test task scheduling problem (FTTSP), which involves assigning suitable aircraft and executing the flight test tasks in a given order. Generally, the flight test duration (FTD) is the primary optimisation objective for the flight test task schedule, as it has a direct impact on aircraft development costs and the time to enter the market. In this study, the FTTSP not only considers FTD but also takes into account task transfer consumption (TTC). A mixed‐integer linear programming mathematical model is first formulated to describe the FTTSP characteristics with the optimisation of the FTD and the TTC in a sequential manner. Then, a memetic algorithm with lexicographic optimisation (MALO) is proposed, which can efficiently obtain a high‐quality solution and ensure that the most critical metric can be fully optimised. In MALO, a two‐vector encoding and a task logic relationship repair mechanism based on the binary tree are established. An idle time insertion decoding method is designed to improve the aircraft utilisation rate. In addition to the selection, crossover and mutation operators, a local search operator is designed to enhance the solution quality. Finally, the full‐scale test instances are generated for the FTTSP to evaluate the algorithm's performance. The numerical results demonstrate the effectiveness and competitiveness of the MALO in generating a high‐quality schedule for flight test tasks.

Enhancing Underwater Object Recognition: Integrating Transfer Learning with Hybrid Optimization Techniques for Improved Detection Accuracy

Underwater object recognition presents unique challenges due to varying water conditions, low visibility, and the presence of noise. This research proposes an advanced methodology that combines transfer learning and hybrid optimization techniques to enhance recognition accuracy in underwater environments. Specifically, a pre-trained EfficientNet model is employed for feature extraction, leveraging its capacity to capture diverse features in underwater images. The model is then optimized using a hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA) to fine-tune hyperparameters such as learning rate, number of layers, and activation functions. This hybrid approach balances exploration and exploitation in the search space, allowing the model to converge on an optimal solution that maximizes accuracy. The model is evaluated against nine existing deep learning models, including ResNet-50, VGG-16, EfficientNet-B0, and MobileNetV2. The proposed PSOGA model achieves a superior accuracy of 98.32%, surpassing the best-performing models like EfficientNet-B0, which reached 95.89%. Furthermore, the model outperforms traditional optimizers like Adam, RMSprop, and AdaGrad, which attained lower accuracies. Precision, recall, and F1-score for the PSOGA model also demonstrate remarkable improvements, highlighting the model's effectiveness in underwater object recognition. The combination of transfer learning and hybrid optimization enables the model to generalize well across diverse underwater environments while maintaining computational efficiency.

An Improved QPSK based on a Hybrid Genetic Algorithm for Efficient OFDM Transmission

An Improved QPSK based on a Hybrid Genetic Algorithm for Efficient OFDM Transmission

Enhancement of Electrical Parameter Extraction from Solar Cells Using a Hybrid Genetic Algorithm with the Levenberg-Marquardt Method

Accurate modeling and simulation of solar photovoltaic (PV) systems are critical for optimizing their performance and efficiency. This requires precise determination of electrical parameters of solar cells, such as photocurrent (Iph), saturation current (I0), series resistance (Rs), shunt resistance (Rsh), and ideality factor (n). Traditional numerical methods for parameter extraction often face limitations in complexity, speed, and assumption dependencies. To address these issues, this study proposes a hybrid method that combines a genetic algorithm with the Levenberg-Marquardt algorithm (GALM) for solar cell parameter extraction. The genetic algorithm provides a robust initial estimate of the parameters, which is then refined by the Levenberg-Marquardt algorithm to achieve high accuracy. The performance of the proposed GALM method is validated using experimental data from a 57-mm silicon solar cell from R.T.C. France. Results indicate that the GALM method achieves one of the lowest RMSE values compared to other optimization techniques, demonstrating its effectiveness in accurately extracting solar cell parameters and closely matching the experimental I-V data. This contributes significantly to optimizing the performance and efficiency of PV systems.

Integrating sequence-dependent setup times and blocking in hybrid flow shop scheduling to minimize total tardiness

This study addresses the minimization of total tardiness in a hybrid flow shop scheduling problem with sequence-dependent setup times and blocking constraints. Each production stage includes multiple machines, and there are no buffers between the stages. The setup time required to process a job depends on the previously processed job. Two mixed-integer linear programming models are developed to formulate the problem. Moreover, an iterative local search algorithm and hybrid genetic algorithms are proposed to have quality solutions with minimal computational efforts. Several computational tests are conducted to tune the heuristic parameters for better performance. Computational experiments are carried out to evaluate the performance of solution methodologies in terms of quality and time. The results indicate that while mixed-integer programming models can solve small-size problem instances, they are not capable of solving large-sized instances. However, the proposed heuristic algorithms find quality solutions for all instances in a very short time.

Memetic Algorithm Optimization of Electric Ship Power System

Memetic Algorithm Optimization of Electric Ship Power System

Mathematical Models and Algorithms for Failed Equipment Planning Replacement

For the operability of deep well pump units under the conditions of oil and gas field equipment rental, it is necessary to plan the required resources. Failed equipment must be replaced with new one. If it is possible to repair the items, they are sent to repair service points. A review of literary sources on the subject of the study and the problem statement are provided. A large number of equipment application points and real operating conditions introduce great uncertainty into planning the failed equipment replacement. The value of the failure rate is a random variable. A mathematical model to manage the process of failed equipment replacing has been developed. Fuzzy logic methods have been used to take into account the uncertainty. Characteristics of fuzzy failure flow have been determined,based on the available information on failures. To perform computational operations, algorithms for working with fuzzy variables have been developed based on, in general case, asymmetric, and parabolic membership functions. The operations of addition, subtraction, multiplication, division, and exponentiation of fuzzy numbers are determined using interval arithmetic andα-cut method. The amount of equipment stock is determined by a fuzzy number. The use of a hybrid genetic algorithm provided an optimal solution to the optimization problem equipment replacement management. The calculation results are given. When solving the fuzzy optimization problem in comparative operations, the rank of the fuzzy number was calculated. The concept of centroid defuzzification of the fuzzy number is used to determine the rank value. The diagram of the fuzzy number membership function and the diagram of the optimization problem objective function: total costs were constructed. Compared with the optimization problem clear formulation, the total costs in the fuzzy formulation turned out to be higher, also the value of the safety stock was determined .

Clustering based on Hybridization of Genetic Algorithm and Improved K-Means (GA-IKM) in an IoT Network

Clustering based on Hybridization of Genetic Algorithm and Improved K-Means (GA-IKM) in an IoT Network

A reinforcement learning based memetic algorithm for energy-efficient distributed two-stage flexible job shop scheduling problem

Given the increasingly severe environmental challenges, distributed green manufacturing has garnered significant academic and industrial interest. This paper addresses the distributed two-stage flexible job shop scheduling problem (DTFJSP) under time-of-use (TOU) electricity pricing, with the objective of minimizing both makespan and total energy consumption costs (TEC). To tackle the problem, a hybrid memetic algorithm (HMA) is proposed. Initially, a three-tier vector encoding scheme and three population initialization strategies are devised. Subsequently, global search operators are tailored to the problem’s characteristics, and seven local search algorithms based on Q-learning are introduced. Additionally, an energy-saving operator is incorporated. Finally, orthogonal experimental design is employed to set algorithm parameters and validate the efficacy of some components. The numerical experimental results demonstrate that the proposed local search operator and energy-saving strategy are effective. Furthermore, the HMA exhibits superior diversity, breadth, and distribution compared to VNS, CMA, and NSGA-II, thereby validating the efficacy of the specialized designs of the HMA in addressing the DTFJSP.

Optimizing Course Scheduling with Genetic Algorithms: A Dynamic Approach

This study explores the application of Genetic Algorithms (GAs) for optimizing course scheduling in educational institutions. Traditional manual scheduling methods are often time-consuming and result in suboptimal solutions due to the complexity and scale of the task. GAs, inspired by natural selection, offer a robust solution by iteratively applying selection, crossover, and mutation to evolve optimal schedules. Using Python and libraries such as NumPy, Matplotlib, and DEAP, the GA was tested through various simulations. The results indicated that GAs significantly improve scheduling efficiency, minimizing conflicts and optimizing resource utilization. Larger populations yielded better fitness values but required more computation time. The hybrid GA approach outperformed manual methods, producing higher quality timetables that adhered better to constraints. A user-friendly interface was developed to facilitate efficient data management and schedule generation. This study confirms that GAs, especially when combined with hybrid techniques, offer a robust solution for the complex problem of course scheduling. Future research should refine these algorithms, explore new hybrid approaches, and address practical implementation challenges to fully harness the potential of GAs in academic scheduling.

MEMO-QCD: quantum density estimation through memetic optimisation for quantum circuit design

This paper presents a strategy for efficient quantum circuit design for density estimation. The strategy is based on a quantum-inspired algorithm for density estimation and a circuit optimisation routine based on memetic algorithms. The model maps a training dataset to a quantum state represented by a density matrix through a quantum feature map. This training state encodes the probability distribution of the dataset in a quantum state, such that the density of a new sample can be estimated by projecting its corresponding quantum state onto the training state. We propose the application of a memetic algorithm to find the architecture and parameters of a variational quantum circuit that implements the quantum feature map, along with a variational learning strategy to prepare the training state. Demonstrations of the proposed strategy show an accurate approximation of the Gaussian kernel density estimation method through shallow quantum circuits illustrating the feasibility of the algorithm for near-term quantum hardware.

Memetic algorithms and dynamic programming on vehicle routing problems in crisis situations

Memetic algorithms and dynamic programming on vehicle routing problems in crisis situations

A Hybrid Genetic Algorithm with Tabu Search Using a Layered Process for High-Order QAM in MIMO Detection

In this paper, we propose a hybrid genetic algorithm (HGA) that embeds the tabu search mechanism into the genetic algorithm (GA) for multiple-input multiple-output (MIMO) detection. We modified the selection and crossover operation to maintain the diverse and wide exploration areas, which is an advantage of the GA, and the mutation operation to perform a local search for a specific region. In the mutation process, the ’tabu’ concept is also employed to prevent the repeated search of the same area. In addition, a layered detection process is applied simultaneously with the proposed algorithm, which not only improves the bit error rate performance but also reduces the computational complexity. We apply the layered HGA (LHGA) to the MIMO system with very high modulation order such as 64-quadrature amplitude modulation (QAM), 256-QAM, and 1024-QAM. Simulation results show that the LHGA outperforms conventional detection approaches. Especially, in the 1024-QAM MIMO system, the LHGA has less than 10% of computational complexity but a 6 dB signal-to-noise ratio (SNR) gain compared to the conventional GA-based MIMO detection scheme.

An effective two-stage memetic algorithm for the dynamic flexible job-shop scheduling problem with job inspection

An effective two-stage memetic algorithm for the dynamic flexible job-shop scheduling problem with job inspection

New Metaheuristics to Solve the Internet Shopping Optimization Problem with Sensitive Prices

In this research, two new methods for solving the Internet shopping optimization problem with sensitive prices are proposed, incorporating adaptive adjustment of control parameters. This problem is classified as NP-hard and is relevant to current electronic commerce. The first proposed solution method corresponds to a Memetic Algorithm incorporating improved local search and adaptive adjustment of control parameters. The second proposed solution method is a particle swarm optimization algorithm that adds a technique for diversification and adaptive adjustment of control parameters. We assess the effectiveness of the proposed algorithms by comparing them with the Branch and Bound algorithm, which presents the most favorable outcomes of the state-of-the-art method. Nine instances of three different sizes are used: small, medium, and large. For performance validation, the Wilcoxon and Friedman non-parametric tests are applied. The results show that the proposed algorithms exhibit comparable performance and outperform the Branch and Bound algorithm.

Joint Optimization of Part Quality Inspection Planning, Buffer Allocation, and Preventive Maintenance in a Serial Manufacturing

This paper addresses the integration of quantity, quality, and maintenance challenges in a serial multistage manufacturing system. This research presents a comprehensive joint optimization model that extensively considers the interdependencies among these aspects. The model encompasses buffer allocation, which efficiently manages stochastic processing times, part quality inspection planning to detect and minimize defects, and preventative maintenance strategies to mitigate machine deterioration. By integrating these functions, the manufacturing system can significantly improve its overall performance and reduce costs, with research results showing an average cost reduction of 15%. The proposed model formulates a stochastic mixed integer nonlinear programming problem, which is NP-hard and combinatorial. To address this problem, a comprehensive solution approach is employed, combining a genetic algorithm as the primary generative method with hybrid genetic algorithm variations. Additionally, simulation (recursive approach) serves as the evaluative method. Computational experiments are conducted to validate and optimize the proposed model. The findings make a substantial contribution to the existing body of knowledge, offering a robust solution that enhances performance and reduces costs in addressing quantity, quality, and maintenance challenges in a serial multistage manufacturing system. Finally, a discussion of the research results, their implications, and applications offers valuable insights for practitioners and decision-makers aiming to optimize their production processes.

A hybridization of grey wolf optimizer and genetic algorithm for the traveling salesman problems

A hybridization of grey wolf optimizer and genetic algorithm for the traveling salesman problems

Niche-based Memetic algorithm with adaptive parameters for optimizing order delivery strategies in O2O platforms

Niche-based Memetic algorithm with adaptive parameters for optimizing order delivery strategies in O2O platforms