Crossover Operator Of Genetic Algorithm Research Articles

Solving the Generalized Traveling Salesman Problem Using Sequential Constructive Crossover Operator in Genetic Algorithm

Solving the Generalized Traveling Salesman Problem Using Sequential Constructive Crossover Operator in Genetic Algorithm

CS-GA-XGBoost-Based Model for a Radio-Frequency Power Amplifier under Different Temperatures.

Machine learning methods, such as support vector regression (SVR) and gradient boosting, have been introduced into the modeling of power amplifiers and achieved good results. Among various machine learning algorithms, XGBoost has been proven to obtain high-precision models faster with specific parameters. Hyperparameters have a significant impact on the model performance. A traditional grid search for hyperparameters is time-consuming and labor-intensive and may not find the optimal parameters. To solve the problem of parameter searching, improve modeling accuracy, and accelerate modeling speed, this paper proposes a PA modeling method based on CS-GA-XGBoost. The cuckoo search (CS)-genetic algorithm (GA) integrates GA's crossover operator into CS, making full use of the strong global search ability of CS and the fast rate of convergence of GA so that the improved CS-GA can expand the size of the bird nest population and reduce the scope of the search, with a better optimization ability and faster rate of convergence. This paper validates the effectiveness of the proposed modeling method by using measured input and output data of 2.5-GHz-GaN class-E PA under different temperatures (-40 °C, 25 °C, and 125 °C) as examples. The experimental results show that compared to XGBoost, GA-XGBoost, and CS-XGBoost, the proposed CS-GA-XGBoost can improve the modeling accuracy by one order of magnitude or more and shorten the modeling time by one order of magnitude or more. In addition, compared with classic machine learning algorithms, including gradient boosting, random forest, and SVR, the proposed CS-GA-XGBoost can improve modeling accuracy by three orders of magnitude or more and shorten modeling time by two orders of magnitude, demonstrating the superiority of the algorithm in terms of modeling accuracy and speed. The CS-GA-XGBoost modeling method is expected to be introduced into the modeling of other devices/circuits in the radio-frequency/microwave field and achieve good results.

AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems

In the context of Industry 5.0, artificial intelligence (AI)-based logistics Unmanned Aerial Vehicles (UAVs) have been widely applied in intelligent transportation systems due to their advantages of faster speed, lower cost, more environment-friendly, and less manpower needed. Whereas, most of the existing logistics UAV delivery models have not taken the energy consumption of the logistics UAVs and mixed time windows of the customers, which leads to their models cannot be applied in practical transportation systems. Therefore, we propose to minimize the total energy cost of multiple logistics UAVs during the customized products delivery period for a smart transportation system. Taking the energy consumption variation of the logistics UAVs, mixed time windows of the customers, as well as simultaneous delivery and pick up into consideration, we formulate a cooperative path planning problem via jointly optimizing the route of the logistics UAVs and the service allocation. To solve this large-scale integer programming problem, we employ the Large Neighborhood Search Algorithm (LNS) to accelerate the convergence rate of Genetic Algorithm (GA), and then develop an improved GA based cooperative path planning algorithm (IGCPA). The optimization procedure of the proposed algorithm IGCPA is divided into two phases, using the GA crossover operator and variational operator in the global search phase and LNS operator in the local search phase, and validating the integer programming model and the effectiveness of the solution algorithm based on different scale cases. Finally, abundant simulation results show that the energy cost of IGCPA is reduced by 17.35%, 15.18% and 9.99% compared with GA, LNS and Particle Swarm Optimization (PSO), respectively. Furthermore, the IGCPA is validated using Solomn standard data, which further verifies that the IGCPA can enhance the convergence rate of GA as well as obtain a lower delivery cost. Sensitivity analysis of the maximum UAV load and battery capacity reveals that the distribution cost tends to decrease and then increase as the increase of maximum load and battery capacity.

A digital-twin-based adaptive multi-objective Harris Hawks Optimizer for dynamic hybrid flow green scheduling problem with dynamic events

In many hybrid flow shop production environments, dynamic events (DEs) often occur and seriously impact production schedules. However, the traditional model rarely considers DEs, which makes the model seriously inconsistent with the actual situation. In addition, to reduce carbon emissions, there is an urgent need to carry out green optimization scheduling of production workshops, especially in emerging digital workshops. First, this paper proposes a dynamic digital-twin-based Multi-Objective Hybrid Flowshop Green Scheduling Model with DEs (MOHFGSM-DEs) to fill the above gaps. The MOHFGSM-DEs model’s bi-objectives are the makespan minimization and digital workshop total energy consumption minimization. In addition, the MOHFGSM-DEs model takes account of representative DEs in the actual dynamic production process, including controlled processing time, device dynamic reconfiguration events, and workpiece reworking events, which makes it more adaptive in practical production. Secondly, an Adaptive Multi-Objective Dynamic Harris Hawks Optimizer (AMODHHO) is presented with DEs parameters perceived by the digital twin of the workshop, adjusting the scheduling scheme adaptively in real time to address the MOHFGSM-DEs model efficiently. AMODHHO combines a nonlinear optimization strategy on balancing the exploration and exploitation of Harris Hawks Optimizer (HHO) and introduces the crossover operator of Genetic Algorithm (GA) to improve its optimal global ability. Moreover, a digital-twin-driven dynamic encoding methodology containing many optimization strategies is designed based on problem-specific characteristics. Finally, numerical experiments and application case comparisons are performed among AMODHHO, SPEA2, and NSGA-II. Results show that AMODHHO is superior to SPEA2 and NSGA-II regarding comprehensive performance. Therefore, the proposed model and AMODHHO are feasible to solve the real-world MOHFGSM-DEs adaptively. Furthermore, it performs dynamic adaptive adjustment for the actual scheduling schemes according to the real-time DEs the digital twin perceives.

A novel crossover operator for genetic algorithm: Stas crossover

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly.

Text Encryption Analysis of Modified Symmetric Keys using Genetic Algorithm on Caesar Cipher and One Time Pad

<span lang="EN-US">The problem of data security is essential because data must be maintained securely and in its integrity, starting with the sending procedure and ending with the intended recipient. One way to maintain data security is to use cryptographic techniques, which use data encryption characteristics to transmit messages in secret (information encoding). Information can be encrypted using the Caesar Cipher and One Time Pad, symmetric key cryptographic algorithms. The key must be changed as a critical component of the cryptographic procedure to enhance the data or text's security. This study aims to analyse text encryption on the Caesar Cipher and One Time Pad (OTP) algorithms using a modified symmetric key by implementing a Genetic Algorithm, an optimisation algorithm. The research phase begins by adjusting the key using two types of Genetic Algorithm crossover operators, one-point crossover and two-point crossover, applied to the Caesar Cipher and OTP algorithms. Then testing and analysis of the ciphertext strength are carried out by comparing the frequency of character repetition with the results of implementing the two modification keys to determine which encoding model provides a better data security strength.</span>

Graph Matching with Distance-Preserving Crossover

Graph models are fundamental to any kind of application on structured real-world problems. Any comparison between graphs by a graph distance measure requires the solution of the inexact graph matching problem, which constitutes a hard combinatorial optimization problem. An inexact matching problem includes in its formulation robustness to any type of perturbation, such as, for instance, noise, inherently present in real-world environments. In this paper, we introduce the concept of distance-preserving crossover operators for genetic algorithms for this task. For large graphs, our algorithm outperforms any state-of-the-art approximate algorithm—in particular, genetic algorithms with alternative crossover operators, which are to the best of our knowledge currently limited to no more than 50 nodes. We use a two-level local search heuristic to further enhance the results, pushing the limits to up to 300 nodes: a first local search step is directly integrated into the crossover operator; another one is applied independently during offspring generation.

Knowledge Application to Crossover Operators in Genetic Algorithm for Solving the Traveling Salesman Problem

Genetic Algorithm often bears with Premature Convergence for solving combinatorial optimization problems but can be improved by modification at different prospectives. In this research, an effective knowledge is applied in the procedure of Genetic Algorithm used for solving TSP. The key concept for the proposed GA is a modification in the crossover operators by applying knowledge of smallest distant cities (shortest edge) assuming that it would improve the process to find the shortest path, by optimizing the new generations. As TSP is represented as a graph, considering cities as nodes and paths as edges, in the proposed approach, crossover point selection for crossover operators is selected upon the basis of minimum edge in the given graph. To test the effect of the proposed method of knowledge application, Linear Order Crossover, Cycle Crossover Operator, and Sequential Constructive Crossover are modified and results are proved on the randomly generated data sets for TSP. This optimization not only improved the shortest path but also reduced the problem of Premature Convergence.

SISTEM PENJADWALAN MATA PELAJARAN SEKOLAH MENGGUNAKAN METODE HYBRID ARTIFICIAL BEE COLONY (HABC)

scheduling is very closely related to teaching and learning activities in a school that aims to support, facilitate, enhance the quality of education and improve discipline in the process of teaching and learning activities. The problem faced in making a schedule of subjects is time, the process of making a schedule takes quite a long time in the process of processing the schedule. Of the algorithms used in scheduling problems, algortima Artificial Bee Colony is an algorithm inspired by the behavior of honeybee colonies that work based on the way bees forage for food. Algortima Artificial Bee Colony is known to have advantages over other optimization algorithms that are very efficient in finding optimal solutions. But the Artificial Bee Colony algorithm has its drawbacks that, if the dimensions of the problem increase, the exchange of information is still limited to one dimension. The weakness of Artificial Bee Colony is what makes the opportunity to develop ABC, namely, Hybrid Artificial Bee Colony (HABC) by adding a crossover operator of genetic algorithms. Genetics algorithm's crossover operator inserted into ABC algorithm to improve exchange of information between bees. It can be concluded that the application of HABC methods can do the optimization process quite well in scheduling issues with fairly low schedule clashing results. Based on the results of trials conducted five times, the results that scheduling applications using the HABC Algorithm can produce a schedule of subjects by clashing to a minimum with an average accuracy percentage rate of 98.03%

REAL TIME SEARCH OF AGRICULTURAL MACHINERY BASED ON MATRIX SEQUENCE SENSOR

Omni-directional vision sensor can provide information within the sensor range, and the directional angle of an object can be accurately obtained through omni-directional images. Based on this characteristic, an automatic navigation and positioning system for agricultural machinery is developed, and a three-dimensional positioning algorithm for agricultural wireless sensor networks based on cross particle swarm optimization is proposed. The method mainly includes three stages: convergence node selection, measurement distance correction and node location. Using the idea of crossover operation of genetic algorithm for reference, the diversity of particles is increased, and the influence of ranging error and the number of anchor nodes on positioning results is effectively improved. The location algorithm has the ability of global search. On the positioning node, the symmetric bidirectional ranging algorithm based on LFM (Linear frequency modulation) spread spectrum technology is used to calculate the distance between the positioning node and each beacon node, and the trilateral centroid positioning algorithm is used to calculate the coordinate position information of unknown nodes. Finally, the Kalman filter algorithm is used to superimpose the observed values of the target state to solve the influence of measurement noise on the positioning accuracy.

Research on Optimization of Heterogeneous Air Cargo Network Based on Hybrid Particle Swarm Algorithm

In order to solve the problem of poor timeliness and high operating cost caused by the failure to consider the types of cargo aircrafts and the lack of scientific and reasonable optimization model in the process of transportation scheduling optimization of China’s air cargo network, a transport network optimization model of heterogeneous cargo aircraft formation is established, which takes the total flight cost and the number of all-cargo aircrafts as two optimization objectives. The model takes into account the demand time window of air nodes, the maximum payload constraint of cargo aircrafts, the dynamic loading and unloading time of cargo aircrafts, the take-off and landing time and the handover time of cargo aircrafts. In this paper, a hybrid particle swarm algorithm is proposed. The crossover operator of genetic algorithm is used to keep the directivity of particles, and the acceptance criterion of simulated annealing is used to enhance the ability of the algorithm to jump out of local optimum. The effectiveness of the algorithm and the model is verified by the simulation optimization and comparative analysis of a numerical example.

Effective Metaheuristic Algorithms for Bag-of-Tasks Scheduling Problems Under Budget Constraints on Hybrid Clouds

Cloud computing is an ideal platform for executing bag-of-task (BoT) applications due to its capability of delivering high-quality and pay-per-use computing services. This paper presents a family of genetic algorithm (GA)-based metaheuristics for scheduling the tasks of data-intensive BoT applications on hybrid clouds. The scheduling objective is to minimize the flowtime of BoT applications under a specified budget constraint. We take into account the impact of communication time and communication cost to formulate the optimization model for the data-intensive BoT scheduling problem. By using a task sequence to represent the scheduling solution, the proposed algorithms start with using a low-complexity strategy to generate an initial solution. The generated initial solution is identified as the best chromosome in the initial population of GA framework. We improve the standard crossover operator in GA’s evolutionary procedure by incorporating a probabilistic model. In addition, we design an efficient task dispatching method to evaluate the scheduling quality of each chromosome. Built upon the improved crossover scheme and task dispatching method, the proposed metaheuristic algorithms employ three crossover operators to solve the BoT scheduling problem considered in this work. Extensive experiments are performed to verify the performance of the proposed algorithms in scheduling data-intensive BoT applications.

A novel completely mapped crossover operator for genetic algorithm to facilitate the traveling salesman problem

As an intelligent search technique, genetic algorithm (GA) is a key member of optimization research methods. Motivated by the ability of GA in resolving complex and nondeterministic polynomial problems, this research proposes a new operator, namely, completely mapped crossover operator to facilitates the search of optimal solutions. Under a very general setting of the state-of-the-art parameters, the performance of newly proposed method is explored with respect to five vibrant and most commonly used techniques existent in the literature. Furthermore, the generality of the applicability of contemporary techniques is maintained by considering eighteen benchmarks from the library of traveling salesman problem, possessing different levels of complexity. Based on diverse performance assessment criteria, we observe that propose method outperforms the contemporary alternatives.

Enhanced firefly optimizer with deep neural network for the detection of epileptic seizures using EEG Signals

Currently, Electroencephalogram (EEG) is extensively used for diagnosing the epilepsy. The objective of this research is to investigate the changes in epilepsy frequency by proposing a new optimization based deep learning model. At first, the EEG recordings were acquired from two online databases; Bern Barcelona (BB), and Bonn University (BU). Then, Chebyshev type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, enhanced firefly optimization technique was proposed for optimizing the extracted features. In the enhanced firefly optimizer, a crossover operator of genetic algorithm was added for enhancing the local convergence rate that gives better classification. At last, the optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). From the experimental simulation, the proposed model improvement maximum of 1.4%, and 8.82% of accuracy in BU and BB EEG datasets, respectively related to the existing models.

An improved quantum particle swarm optimization algorithm for environmental economic dispatch

Consumption of traditional fossil energy has promoted rapid economic development and caused effects such as climate warming and environmental degradation. In order to solve the problem of environmental economic dispatch (EED), this paper proposes a DE-CQPSO (Differential Evolution-Crossover Quantum Particle Swarm Optimization) algorithm based on the fast convergence of differential evolution algorithms and the particle diversity of crossover operators of genetic algorithms. In order to obtain better optimization results, a parameter adaptive control method is used to update the crossover probability. And the problem of multi-objective optimization is solved by introducing a penalty factor. The experimental results show that: the evaluation index and convergence speed of the DE-CQPSO algorithm are better than QPSO (Quantum Particle Swarm Optimization) and other algorithms, whether it is single-objective optimization of fuel cost and emissions or multi-objective optimization considering both optimization objectives. A good compromise value is verified, which verifies the effectiveness and robustness of the DE-CQPSO algorithm in solving environmental economic dispatch problems. The study provides a new research direction for solving environmental economic dispatch problems. At the same time, it provides a reference for the reasonable output of the unit to a certain extent.

Optimal UPQC location in power distribution network via merging genetic and dragonfly algorithm

Nowadays, flexible alternating current transmission system devices, particularly unified power quality conditioner (UPQC) are found to have significant impacts on stability of rising power system. In power systems, several intellectual optimization methods were exploited to position the UPQC. However, those optimization models fail to offer more reliability and feedback signal. Hence, this paper presents a power quality improvement model, which is based on a hybrid algorithm that links genetic algorithm (GA) and DragonFly algorithm (DA). In the current research work, the optimal solution is determined based on the crossover operation of GA in dragonfly algorithm (DA), and hence, the adopted model is named as Genetically Modified DA algorithm. Moreover, the proposed model discovers the optimal location of UPQC device by focusing on the UPQC cost, power losses, and Voltage stability Index. The proposed model is carried out in IEEE 69, and IEEE 33 test bus systems. In addition, the performance of implemented model is distinguished over other conventional models such as artificial bee colony, firefly, grey wolf optimization, whale optimization algorithm, worst solution linked whale optimization algorithm update (WS-WU), GA and DA. The performance of the proposed model is effectively proved by performance and convergence analysis.

Balanced crossover operators in Genetic Algorithms

Abstract In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: “left-to-right” and “shuffled”), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators perform better than one-point crossover. Furthermore, in two out of three crossovers, the “left-to-right” version performs better than the “shuffled” version.

Cost-Driven Off-Loading for DNN-Based Applications Over Cloud, Edge, and End Devices

Currently, deep neural networks (DNNs) have achieved a great success in various applications. Traditional deployment for DNNs in the cloud may incur a prohibitively serious delay in transferring input data from the end devices to the cloud. To address this problem, the hybrid computing environments, consisting of the cloud, edge, and end devices, are adopted to offload DNN layers by combining the larger layers (more amount of data) in the cloud and the smaller layers (less amount of data) at the edge and end devices. A key issue in hybrid computing environments is how to minimize the system cost while accomplishing the offloaded layers with their deadline constraints. In this article, a self-adaptive discrete particle swarm optimization (PSO) algorithm using the genetic algorithm (GA) operators is proposed to reduce the system cost caused by data transmission and layer execution. This approach considers the characteristics of DNNs partitioning and layers off-loading over the cloud, edge, and end devices. The mutation operator and crossover operator of GA are adopted to avert the premature convergence of PSO, which distinctly reduces the system cost through enhanced population diversity of PSO. The proposed off-loading strategy is compared with benchmark solutions, and the results show that our strategy can effectively reduce the system cost of off-loading for DNN-based applications over the cloud, edge and end devices relative to the benchmarks.

A Comparative Study of Eight Crossover Operators for the Maximum Scatter Travelling Salesman Problem

The maximum scatter traveling salesman problem (MSTSP), a variation of the famous travelling salesman problem (TSP), is considered here for our study. The aim of problem is to maximize the minimum edge in a salesman’s tour that visits each city exactly once in a network. It is proved be NP-hard problem and considered to be very difficult problem. To solve this kind of problems efficiently, one must use heuristic/metaheuristic algorithms, and genetic algorithm (GA) is one of them. Out of three operators in GAs, crossover is the most important operator. So, we consider eight crossover operators in GAs for solving the MSTSP. These operators have originally been designed for the TSP which can also be applied on the MSTSP after some modifications. The crossover operators are first illustrated manually through an example and then executed on some well-known TSPLIB instances of different types and sizes. The obtained comparative study clearly demonstrates the usefulness of the sequential constructive crossover operator for the MSTSP. Finally, a relative ranking of the crossover operators is reported.

Research on Assembly Sequence Planning of Marine Diesel Engine Based on Improved Genetic Algorithm

That genetic algorithm is applied to the assembly sequence planning of marine diesel engine can improve assembly planning efficiency a lot. The genetic algorithm crossover operator of genetic algorithm is improved. By combining the clashes matrix and priority relationship matrix, the relational-clashes matrix is established. In order to get the assembly sequence which can assembly safely and effectively, but conform to the actual assembly habits as well, the fitness function combines the feasibility of assembly sequence, the rationality of assembly sequence and the number of redirection. The evaluation method based on the general principle of assembly sequence was proposed for the first time. Priority of parts is evaluated according to their barycentric coordinates and precision level. The evaluation method can enrich the evaluation method of assembly sequences and expand the application of intelligent algorithms in the field. After some tests and analysis of the experimental results, the algorithm proved to be convergent and the results are effective.