Simple Genetic Algorithm Research Articles

Multi-objective multi-population simplified swarm optimization for container loading optimization with practical constraints

The container loading problem (CLP) holds significant importance in logistics and supply chain management due to its direct influence on transportation costs and times, thereby impacting the competitive advantages of enterprises across industries. Although there are existing studies for CLP, there remains a gap in addressing the optimization of multiple objectives while satisfying practical constraints. Moreover, container loading methods must also meet performance requirements such as high computational speed, explainability, and implementation capability. Although meta-heuristic-based algorithms have shown effective computational capabilities and performance, such algorithms often being trapped in the local optima especially when searching in the vast solution space of the CLP, particularly as the quantity and diversity of cargo sizes and shapes increase. Motivated by realistic needs, this study aims to develop a UNISON-based framework that integrates the merging spaces algorithm, a novel simple but effective hybrid simplified swarm optimization genetic algorithm (SSO-GA), and a multi-populations co-evolution strategy to determine the loading sequence of parcels to maximize space utilization and weight balance while satisfying to practical constraints. Specifically, the merging spaces algorithm merges fragmented small spaces resulting from loaded parcels into larger spaces, thereby facilitating the accommodation of additional parcels. The hybrid SSO-GA splits encoded solutions and updates segment using novel strategies resulting in the rearrangement of a group of parcels and their corresponding layouts for better space utilization. Furthermore, the multi-populations co-evolution strategy enhances the diversity of the search space and stabilizes solution quality by simultaneously exploiting the best solutions and exploring alternative solutions during the solution update process. An empirical study was conducted by using a public benchmark dataset which demonstrated the practical effectiveness of the proposed framework by significantly improving average space utilization while ensuring center balance and satisfying practical constraints. Furthermore, this study can also serve as a digital support system capable of assisting decision-makers in optimizing container loading operations, thereby improving productivity and saving valuable time.

Cover Feature: On the Synthesis and Structure of ‘Naked’ Ga(I) and In(I) Salts and the Surprising Stability of Simple Ga(I) and In(I) Salts in the Coordinating Solvents Ether and Acetonitrile (Chem. Eur. J. 35/2024)

Cover Feature: On the Synthesis and Structure of ‘Naked’ Ga(I) and In(I) Salts and the Surprising Stability of Simple Ga(I) and In(I) Salts in the Coordinating Solvents Ether and Acetonitrile (Chem. Eur. J. 35/2024)

On the Synthesis and Structure of 'Naked' Ga(I) and In(I) Salts and the Surprising Stability of Simple Ga(I) and In(I) Salts in the Coordinating Solvents Ether and Acetonitrile.

In this work, we present the solid-state structures of solvent-free Ga[pf] and In[pf] salts ([pf]-=[Al(ORF)4]-; RF=C(CF3)3), which are very rare examples of salts with truly 'naked' metal cations. Both salts may serve as starting materials for subvalent gallium and indium chemistry with very weakly coordinating ligands providing the freedom of choice for solvents and ligands for the future. On the other hand, we report and rationalize the formation and isolation of [M(OEt2)2][pf] and [M(MeCN)2][pf] (M=Ga, In), underlining the surprising stability of these subvalent group 13 M+ ions against disproportionation. Unexpectedly, dicoordinate and carbene analogous [M(L)2]+ ions with the [pf]- counterion are stable in L=acetonitrile and diethyl ether at room temperature, opening up possible applications for example in organic synthesis and catalysis.

Utilization of a Genetic Algorithm to Identify Optimal Geometric Shapes for a Seismic Protective Barrier

The utilization of seismic barriers for protection against the hazardous impact of natural or technogenic waves is an extremely promising emerging technology to secure buildings, structures and entire areas against earthquake-generated seismic waves, high-speed-transport-induced vibrations, etc. The current research is targeted at studying the effect of seismic-barrier shape on the reduction of seismic-wave magnitudes within the protected region. The analytical solution of Lamb’s problem was used to verify the adopted numerical approach. It was demonstrated that the addition of complementary geometric features to a simple barrier shape provides the possibility of significantly increasing the resulting seismic protection. A simple genetic algorithm was employed to evaluate the nontrivial but extremely effective geometry of the seismic barrier. The developed approach can be used in various problems requiring optimization of non-parameterizable geometric shapes. The applicability of genetic algorithms and other generative algorithms to discover optimal (or close to optimal) geometric configurations for the essentially multiscale problems of the interaction of mechanical waves with inclusions is discussed.

Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA)

Combinatorial Optimization of Physics Parameterization Schemes for Typhoon Simulation Based on a Simple Genetic Algorithm (SGA)

Effective Test Cases Generation with Harmony Search and RBF Neural Network

Software testing is one of the integral activities during development of software products. Generation and selection of the test cases either in static or dynamic form play pivot role for ensuring the quality of software products. There are numerous approaches in the literature for automatic generation of test cases but coverage criteria and fault detection rate are prominent metrics for checking the effectiveness of the software products during testing phase of software development. In the present, a new Harmony Radial Testing (HRT) is proposed by combining the concepts of Harmony Search Algorithm (HSA) and Radial Basis Function-Neural Network (RBF-NN) approaches. The main objective of the proposed HRT method is to generate automatic test cases by considering the criteria of branch coverage with improvement in the Maximum branch Coverage (MaxC), Average Coverage (AC) and Average Percentage Fault Detection (APFD) rates. The proposed approach combined with the Radial Basis Function (RBF), denoted as a HRT approach. The proposed approach is used to optimize harmony search over the randomly selected sample test cases, training the RBF-NN to simulate the fitness function. Seven Python codes have been tested through proposed approach and computed results are compared with Primal-Dual Genetic Algorithm (PDGA), Simple Genetic Algorithm (SGA) and random methods. It is observed that the proposed HRT algorithm optimizes consistently yielded reliable results, which may be used in future for enriching the software testing process by the software industries.

Hybrid System for Image Restoration

The image processing field is considered one of the highly sensitive fields for accuracy due to the quality of the processing in view of the visual view of the user and due to the development in modern means of communication and the use of these means in the transfer of images and the impact of these means on several factors, including external, including those related to the quality of the source signal and the impact of the transmitted images by these conditions, digital correction processes have emerged to reach a high quality of the received image. Most of the studies and research on digital image correction have focused on the quality and time required for correction processes, and some have focused on using traditional optimization algorithms to obtain acceptable visual quality, while others have focused on shortening time regardless of quality, and due to the fact that all studies and research that have been viewed were focused on the use of speculative methods and hybrid algorithms to address distortion in images, as all weaknesses were related to time, quality and calculations because the size of the image data is large Very. The research aims to study digital images and then process images, optimization methods, genetic algorithms and accomplish an algorithm with high features. In this paper, the simple genetic algorithm is used in the process of correcting images of the type (.JPG), as this method is characterized by the fact that it includes many of the advantages of the previous methods in addition to additional features that provided quality, accuracy and shortening time in calculations. The paper has been completed in five phases: The first stage: Providing external protection for the system by entering the password. Second Stage: Creating the system's database. Third stage: Create (code book) in a new style based on the size of the file used. Fourth stage: Building the genetic algorithm for correction Fifth stage: Using a mathematical model to add distortion to a clear image, correct it and compare the results.

Optimization of Adaptive Active Support Parameters for Large Deformation Prestressed Anchor Ropes in Soft Rock Tunnels Using Genetic Algorithm Based Computer Applications

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward.

Cationic dinuclear complexes [M2(PCP)2μ-Cl][GaCl4] of the group 10 elements. metallophilic interactions and catalytic dehydrogenation of Me2NHBH3.

The facile synthesis of the cationic dinuclear group 10 complexes [M2(PCP)2μ-Cl]+ (M = Ni, Pd, Pt) by transmetallation from a simple Ga precursor is reported (PCP = 2,6-(Ph2P)C6H3). Their use for the catalysed dehydrogenation of Me2NHBH3 shows that Ni has a higher reactivity than Pt, whereas Pd is inactive.

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Wavelet neural networks have been widely applied to dynamical system identification fields. The most difficult issue lies in selecting the optimal control parameters (the wavelet base type and corresponding resolution level) of the network structure. This paper utilizes the advantages of Legendre multiwavelet (LW) bases to construct a Legendre multiwavelet neural network (LWNN), whose simple structure consists of an input layer, hidden layer, and output layer. It is noted that the activation functions in the hidden layer are adopted as LW bases. This selection if based on the its rich properties of LW bases, such as piecewise polynomials, orthogonality, various regularities, and more. These properties contribute to making LWNNs more effective in approximating the complex characteristics exhibited by uncertainties, step, nonlinear, and ramp in the dynamical systems compared to traditional wavelet neural networks. Then, the number of selection LW bases and the corresponding resolution level are effectively optimized by the simple Genetic Algorithm, and the improved gradient descent algorithm is implemented to learn the weight coefficients of LWNN. Finally, four nonlinear dynamical system identification problems are applied to validate the efficiency and feasibility of the proposed LWNN-GA method. The experiment results indicate that the LWNN-GA method achieves better identification accuracy with a simpler network structure than other existing methods.

Solving 2D damped Kuramoto-Sivashinsky with multiple relaxation time lattice Boltzmann method

Lattice Boltzmann method (LBM) is a powerful fluid flow solver. Using this method to solve other PDEs might be a difficult task. The first challenge is to find a suitable local equilibrium distribution function (EDF) capable of recovering the desired PDE. The next difficulty arises from the explicit nature of LBM. The conditional stability of the LBM algorithm affects the numerical solution accuracy. Damped Kuramoto–Sivashinsky (DKS) equation is a fourth–order PDE that recently challenged many numerical methods' abilities. This equation is highly sensitive to a parameter that causes three states of solutions in a small interval of freedom. In this paper, we challenged LBM to solve the two–dimensional DKS equation by finding EDF using the Chapman–Enskog analysis up to the fourth–order. Also, the von Neumann analysis and a simple genetic algorithm are applied to find reliable values for the free parameters. Furthermore, a modification on image-based ghost node method is proposed for implementation of the boundary conditions in the complex geometries.

Differentially Private Simple Genetic Algorithms

The differentially private (DP) selection problem is a fundamental building block in the private literature that is commonly solved with the exponential mechanism. It is well known that efficiency is the major drawback of the exponential mechanism, as the utility function must be computed for all elements in the domain. Genetic algorithms (GAs) use the principles of evolution in nature to efficiently search through large domains and select the best candidate. We observe that GAs have many appealing properties for DP Selection. These include being robust to noisy objectives, placing no restriction on the utility function, and efficient runtime for large domains. However, prior work investigating DP GAs has shown poor utility in practice and often gives the highest utility when zero generations are conducted (indicating that GA operations are not beneficial under DP). This work provides a new DPGA based on the simple GA that addresses the weaknesses of prior solutions. We reduce the destructive nature of previous GA operators and utilize several techniques to reduce the noise from DP. Our modifications allow us to utilize the GA operators over multiple generations (under DP) and improve the GA's overall utility over zero generation techniques. Our work shows that private GAs are competitive with state-of-the-art general and problem-specific solutions to the DP selection problem, with runtime sublinear in the domain size.

Quantitative assessment on synchronisation capability of a line‐start permanent magnet synchronous motor with hybrid rotor

AbstractThe authors devote a quantitative assessment on the synchronisation capability of a line‐start permanent magnet synchronous motor (LSPMSM) equipped with a hybrid rotor. The quantitative assessment is realised by determining a load criterion consisting of load torques and inertias under which the motor will just synchronise. During the process of load‐criterion determination, close attention is paid to parameters governing the motor synchronisation behaviour. Following the principle of stator‐current error minimisation, the time‐variant magnetising inductances and permanent magnet (PM) flux linkages of the analysed LSPMSM are simplified into slip‐dependent constants via the simple and robust genetic algorithm (GA). The load criterion is then obtained after the establishment of a dynamic motor model involving the simplified parameters. Prototyping tests focused on motor speed‐time responses under different mechanical loads are carried out to validate the obtained load criterion and the paper analyses.

On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

On-time and energy-saving train operation is important for the sustainable development of rail transit. As for the problems of traction energy consumption and on-time arrival at stations faced by trains in rail transit, an optimization strategy of energy-saving speed curves of trains based on an improved adaptive genetic algorithm (AGA) was proposed in this paper. First, weight coefficients of operation time and energy consumption were designed through an analytic hierarchy process, and an optimization model that targets train operation time and energy consumption was established according to a basic train operation model with constraints such as speed limits and precise train stopping. Then, on-time and energy-saving speed curves of trains were generated based on the improved AGA. Finally, a simulation was carried out with actual rail transit lines. The results show that the proposed method has strong efficiency for energy conservation and better optimization performance than the simple genetic algorithm in solving train trajectory optimization problem.

Guided Intelligent Hyper-Heuristic Algorithm for Critical Software Application Testing Satisfying Multiple Coverage Criteria

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert’s decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods.

Framework for identifying network attacks through packet inspection using machine learning

Abstract In every network, traffic anomaly detection system is an essential field of study. In the communication system, there are various protocols and intrusions. It is still a testing area to find high precision to boost the correct distribution ratio. Many authors have worked on various algorithms such as simple classification, K-Means, Genetic Algorithm, and Support Vector Machine approaches, and they presented the efficiency and accuracy of these algorithms. In this article, we have proposed a feature extraction technique known as “k-means clustering,” which has its roots in signal processing and is employed to divide a set of n observations into k clusters, each of which has its origin from the observation with the closest mean. K-Means method is applied in this study to investigate the stream and its implementation and applications using Python and the dataset on the KDDcup99. The effectiveness of the outcome indicates the planned work’s efficiency in relation to other widely available alternatives. Apart from the applied method, a web-based framework is designed, which can inspect an actual network traffic packet for identifying network attacks. Instead of using a static file for testing the network attack, a web page-based solution uses database to collect and test the information. Real-time packet inspection is provided in the proposed work for identifying new attacks.

Parallel Mirrors Based Marine Predator Optimization Algorithm with Deep Learning Model for Quality and Shelf-Life Prediction of Shrimp

Automatic classification and assessment of shrimp freshness plays a major role in aquaculture industry. Shrimp is one of the highly perishable seafood, because of its flavor and excellent nutritional content. Given the high amount of industrial production, determining the freshness of shrimp quickly and precisely is difficult. Instead of using feature-engineering-based techniques, a novel hybrid classification approach is proposed by combining the strength of convolutional neural networks (CNN) and Marine Predators Algorithm (MPA) for shrimp freshness diagnosis. In order to choose the best hyperparameter values, marine predator algorithm is improved using Parallel Mirrors Technique (PMPA). The proposed methodology employs a pretrained CNN model termed EfficientNet (ENet), which is combined with the PMPA algorithm to form the PMPA-ENet architecture. The proposed approach yields high performance while also reducing computational complexity. The result showed that proposed model achieved an accuracy and F-score of 98.62% and 97.25% for assessment of freshness in shrimp. PMPA's effectiveness in determining optimal values is compared to four different meta-heuristic algorithms hybridized with ENet including Particle Swarm Optimization (PSO), Simple Genetic Algorithm (SGA), Whale Optimization Algorithm (WOA), and traditional Marine Predator Algorithm (MPA). The results indicated that PMPA-ENet algorithm provides better classification compared with other algorithms

RADIOTHERAPY EVOLUTIONARY ALGORITHM FURTHER 2D PARETO-MULTI OBJECTIVE OPTIMIZATION WITH BIOLOGICAL EFFECTIVE MODEL FOR HEAD-NECK CANCER HYPERFRACTIONATED TREATMENT

Constrained algorithms for BED model (Biological Effective Dose) in Head and Neck tumors HyperfractionatedTPO optimized with Pareto-Multiobjective (PMO) Genetic Algorithms (GA) software are obtained. The mathematical method for constrained GA is applied for a number of series of Pareto Functions. Results demonstrate PMO-AI imaging process sequences and extensive numerical values of PMO Head and Neck cancer parameters. Comparison and review with simple constrained GA Optimization is presented. Improved RT Head and Neck cancer TPO, and tumors in general for Fractional-dose photon dose delivery are explained in brief.

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

The Successive Approximation Genetic Algorithm (SAGA) for Optimization Problems with Single Constraint

A limited feasible region restricts individuals from evolving optionally and makes it more difficult to solve constrained optimization problems. In order to overcome difficulties such as introducing initial feasible solutions, a novel algorithm called the successive approximation genetic algorithm (SAGA) is proposed; (a) the simple genetic algorithm (SGA) is the main frame; (b) a self-adaptive penalty function is considered and the penalty factor is adjusted automatically by the proportion of feasible and infeasible solutions; (c) a generation-by-generation approach and a three-stages evolution are introduced; and (d) dynamically enlarging and reducing the tolerance error of the constraint violation makes it much easier to generate initial feasible solutions. Then, ten benchmarks and an engineering problem were adopted to evaluate the SAGA in detail. It was compared with the improved dual-population genetic algorithm (IDPGA) and eight other algorithms, and the results show that SAGA finds the optimum in 5 s for an equality constraint and 1 s for an inequality constraint. The largest constraint violation can be accurate to at least three decimal fractions for most problems. SAGA obtains a better value, of 1.3398, than the eight other algorithms for the engineering problem. In conclusion, SAGA is very suitable for solving nonlinear optimization problems with a single constraint, accompanied by more accurate solutions, but it takes a longer time. In reality, SAGA searched for a better solution along the bound after several iterations and converged to an acceptable solution in early evolution. It is critical to improve the running speed of SAGA in the future.