Multi-point Crossover Research Articles

The Optimization of Urban Traffic Routes Using an Enhanced Genetic Algorithm: A Case Study of Beijing South Railway Station

Optimizing travel routes in urban transportation networks is essential for enhancing traffic efficiency in major cities. This study focuses on optimizing the combined subway and taxi travel routes from Beijing Union University to Beijing South Railway Station, a critical route for students and staff in Beijing. To address this, we propose three genetic algorithms: the Standard Genetic Algorithm (SGA), the Multipoint Crossover and Elitist Selection Genetic Algorithm (MPEGA), and the Improved Dynamic Crossover and Mutation Rate Genetic Algorithm (IDCMGA). Experimental results indicate that MPEGA reduces the mean travel cost by 15.21%, the variance by 81.72%, and the standard deviation by 57.25% compared to SGA. Additionally, IDCMGA reduces the average by 1.76%, the variance by 63.16%, and the standard deviation by 39.30% compared to MPEGA. The improved IDCMGA algorithm demonstrates significant advantages over the other two algorithms in terms of global optimization capability, convergence speed, and stability, and is more adept at adapting to new traffic conditions to identify the optimal route. Optimizing this route not only reduces commuting time and costs but also alleviates traffic congestion, thereby enhancing the overall efficiency of the urban transportation system.

Component importance preference-based evolutionary graph neural architecture search

Recently, graph neural architecture search (GNAS) has become an increasingly hot research topic as a promising technique for automatically searching graph neural networks (GNNs) with no or little domain expertise. The search space and optimization strategy are the core factors of GNAS. However, the search space of existing GNAS methods is limited, and their optimization strategies treat components indiscriminately. In the current study, a more expressive search space is first designed. Subsequently, it is illustrated that components contribute different importance to data-specific tasks or datasets, and this study assumes component importance as a probability parameter. To this end, a component importance preference-based evolutionary GNAS method (called CIPE) is proposed. CIPE defines component importance and its preference selection and updating method. Subsequently, the designed importance preference-guided multipoint crossover and multistrategy mutation operators are applied to the evolutionary process. Finally, the effectiveness of CIPE is verified for transductive and inductive tasks. The experimental results demonstrate the validity of the component importance assumption and the superiority of CIPE compared with the state-of-the-art handcrafted GNNs and GNAS methods on all eight datasets. The mean accuracy obtained by CIPE on the datasets Cora, CiteSeer, PubMed, Cornell, Texas, Wisconsin, and Chameleon is 83.84%, 73.23%, 80.28%, 78.38%, 86.49%, 82.35%, and 73.59%, respectively. Specifically, the mean accuracy is improved by 2.71% and 5.28% on the datasets Texas and Chameleon, respectively. The mean F1-score obtained by CIPE on the dataset PPI is 99.37%, with an improvement of 0.24%. The code is available at https://github.com/chnyliu/CIPE.

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm

With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelligence algorithm is an optimization algorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarm intelligence algorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimization algorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimization algorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining species diversity. Finally, the global optimal network structure with the highest score is obtained based on the elimination-dispersal activity strategy. Multiple tests and comparison experiments were conducted on 10 sets of benchmark test functions, two non-temporal and temporal data types, and six data samples of two benchmark 2T-BN networks to evaluate and analyze the optimization performance and structure learning ability of the proposed algorithm under various data types. The experimental results demonstrated that IBFO-A exhibits good convergence, stability, and accuracy, whereas IBFO-D is an effective approach for learning DBN structures from data and has practical value for engineering applications.

Applying Developed Genetic Algorithm Operators to the Knapsack Problems

This study investigated the effect of the previously developed random mixed crossover (RMC), back controlled selection (BCSO), double directions sensitive mutation operators (DDSM), and backward controlled termination criteria (BCTC) on the performance of a genetic algorithm (GA). In the first study, the following three benchmark 0-1, bounded, and unbounded knapsack problems problems were analyzed. In the first stage, the existing operators namely; multi-point crossover operator and tournament selection operator, 1% mutation ratio, and the fitness convergence termination criteria were applied to the benchmark problems. In the second stage of the study, the analysis was conducted by separately applying the previously developed operators, RMC, BCSO, DDSM, and BCTC to the same benchmark problems, and the results obtained from the analysis in this stage were compared with those obtained from the first stage. In the third stage of the study, the previously developed RMC, BCSO and DDSM operators, and BCTC were applied to the same benchmark problems in the same analysis, and the results were compared with those obtained from the first stage. The results of the analysis showed that when the developed operators (crossover, selection and mutation) and termination criteria were collectively used, they were more successful than the existing operators and the developed operators that were separately applied to the benchmark problems.

Image encryption algorithm based on 2D hyper-chaotic system and central dogma of molecular biology

Abstract With the widespread use of images, image security has received much attention. Image security can be guaranteed by encrypting the plain image and transmitting the corresponding cipher image. This paper proposes an image encryption algorithm based on the novel two-dimensional (2D) hyper-chaotic system, bidirectional diagonal crossover transformations (BDCTs) and central dogma of molecular biology. Firstly, six chaotic sequences are generated using the proposed hyper-chaotic system, which are used in the permutation and diffusion processes. Secondly, the 5th–8th bit-planes of plain image are permuted using the designed BDCTs. Thirdly, the permuted image is dynamically encoded into a quaternary DNA sequence, and then the quaternary DNA sequence is transcribed into a quaternary RNA sequence. After that, the quaternary RNA sequence is diffused using quaternary RNA operations. Finally, the diffused RNA sequence is translated into a codon sequence, and then the designed codon-level multipoint crossover scheme is used to further improve the diffusion effect. Experiment results and security analyses demonstrate that our algorithm has high security and efficiency. In other words, our algorithm is quite suitable for real-time image cryptosystems.

Reinforced Two-Stream Fuzzy Neural Networks Architecture Realized With the Aid of One-Dimensional/Two-Dimensional Data Features

A novel structure of reinforced two-stream fuzzy neural networks (TSFNNs) realized with the aid of fuzzy logic and transfer learning method is presented. This architecture consists of a TSFNN and a fusion strategy. TSFNN architecture consists of two combined networks of both fuzzy rules-based radial basis function neural networks (FRBFNN) and convolutional neural networks (CNNs). In the TSFNN architecture, one stream employs the deep CNN to extract the spatial information of images and effectively learn the high-level features and another stream uses the FRBFNN to analyze the distribution of data points over the input space and learn to capture complex relationships in data. In the fusion strategy, the outputs of two streams are concatenated by a softmax function, which normalizes the output to a probability distribution. A transfer learning method is considered to reconstruct new data representation as the inputs of CNN to mine potential spatial features of data. Moreover, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization is used to alleviate the possible overfitting and enhance the generalization ability. The proposed method not only inherits the advantages of FRBFNN and CNN such as global feature extraction ability, good local approximating performance, ability of handling uncertainty by fuzzy logic but also improves the classification performance under the synergy between two-stream architecture and the fusion strategy. Experimental results obtained for a diversity of datasets as well as partial discharge datasets be using in the real life of fault diagnosis and black plastic wastes datasets for recycling confirm the effectiveness of the proposed TSFNN. A comprehensive comparative analysis is covered. This design can simultaneously capture different level information of inputs and easing the insufficient problem of extracting features from a single steam. Especially, we show that the synergistic effect of FRBFNN, CNN, enabling deep learning for generic classification tasks and multipoint crossover, and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization can effectively improve the performance of the TSFNNs.

Multiobjective design of water distribution networks using modified NSGA-II algorithm

Abstract Optimally designed water distribution networks (WDNs) make engineers’ tasks difficult due to various challenges like non-linearity between head-loss and flow, commercially available distinct diameters, combinatorial, nondeterministic polynomial-time hard problems and a large number of decision variables. This paper develops a new hybrid NSGA-II algorithm augmented with a random multi-point crossover operator and a local search denoted by RLNSGA-II to design the multiobjective WDN. The efficiency of the proposed algorithm (RLNSGA-II) is tested on three benchmark problems, namely New York, Hanoi and Balerma networks. The results obtained are compared with the best-known algorithms available in the literature. The results have shown that the proposed algorithm RLNSGA-II has found better converged and distributed solutions for all three representative benchmark problems considered in the literature consistently and evidently when compared with the best-known approximation of solutions published. Furthermore, as the complexity of the WDN increases, its advantages over other algorithms become more significant.

Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary algorithm (OBL-KnEA) is proposed to improve the performance of model solving. The algorithm uses opposition-based learning to generate initial populations for faster convergence. Furthermore, a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range. By comparing the experiments with other excellent algorithms on HCTSO, OBL-KnEA achieves excellent results in terms of evaluation metrics, initial populations, and model optimization effects.

Thermodynamic Optimization of Aircraft Environmental Control System Using Modified Genetic Algorithm

This paper presents an optimization method for the civil aircraft environmental control system (ECS) mainly involving two airstreams: the ram airstream for cooling and the bleed airstream for supplying the cabin. The minimum total fuel energy consumption rate (FECR), defined as the weighted sum of the shaft power extraction and propulsive power loss, is obtained under the precondition of the constant outputs in the cooling capacity and outlet pressure. A modified genetic algorithm (GA) is proposed to acquire the optimal values of the heat transfer areas, temperature ratio of bleed air, mass flow rate of ram air, and pressure ratios of the turbine, compressor, and fan. The statistical results show that the multipoint crossover and continuity improvement implemented in the modified GA improve convergence and distribution performance. The probability of reaching a satisfactory result using modified GA is 62.4% higher than standard GA. Due to the decrease of inlet parameters of bleed air and the elimination of power input in the compressor, the FECR of the optimization case can be lowered by 11.0%. In general, the evaluation method considering energy quality together with the modified optimization technique is proved effective in energy-saving design for such energy systems such as ECS with multiple inputs and outputs.

An improved genetic algorithm for solving flexible job shop

According to the characteristics of flexible job shop scheduling (FJPS), a mathematical model was established to minimize the maximum completion time, and an improved genetic algorithm was proposed to solve the problem. A variety of heuristic methods are used to improve the quality of the initial solution. The parallel double-chain encoding is designed and the optimal insertion method is proposed to improve the quality of the solution. Two crossover methods, namely IPOX crossover and multi-point crossover, are adopted to inherit the excellent genes from the parent generation and balance the global development ability of the algorithm. In different coding layers, a variety of variation methods were used to maintain the diversity of the population. The local development ability of the algorithm is enhanced by variable neighborhood search. Finally, by solving the Brandimarte standard example and comparing with other algorithms, the feasibility and effectiveness of the proposed algorithm are verified.

Genetic and chaotic signatures in offspring – an encrypted generation of image family

With the rapid escalation of information technology and the internet, the digital image has turned out to be a vital medium for communication. Hence, there is an increasing demand to protect these digital images as they are transmitted over the insecure medium such as the Internet. This paper proposes the Genetic Algorithm influenced image encryption scheme. Both crossover and mutation operations were performed to enhance the statistical measures of the grayscale cipher images. Intra-pixel bit manipulation improved the diffusion property during mutation. Crossover accomplished the generation of offspring with the confluence of image intensities and keys. The chaotic Logistic and Tent maps provided improvement in keyspace through their role in the generation of initial seeds. Noticeably, the initial seeds were generated from the features of input image such as minimum & maximum number of occurrences of intensity and minimum & maximum intensity levels. In this regard, every image will accompany a unique key sequence as a session key which needs to be shared with the intended receiver for the distortion-free recovery of original images. Besides, the multi-point genetic crossover was employed for diffusion which resulted in the production of a couple of offspring. The Fitness test which decides the number of generations was executed by performing correlation and entropy analyses at a threshold of 0.01 and 7.99 for the former and later respectively. The investigational consequences authenticate that the proposed scheme not only reveals simple and optimised encryption, it is also defending against various distinctive attacks. The proposed image security solutions can be incorporated in banking, medical insurances, e-healthcare, and e-governance sectors for document confidentiality and integrity checking mechanisms.

Multiobjective Endmember Extraction Based on Bilinear Mixture Model

Hyperspectral imagery is always composed of mixed pixels because of the limited spatial resolution of a sensor and the macroscopic/microscopic mixture of distinct substances. The linear mixing model (LMM) is proven to be simple and effective in extensive literature when the macroscopic mixture dominates the mixing process. But when the photons undergo multiple reflections before reaching the sensor, the LMM becomes invalid. In this circumstance, the bilinear mixture model (Bi-LMM), which considers secondary reflections with a bilinear term, is a viable alternative. However, the bilinear term in most existing Bi-LMMs is constructed based on the pre-estimated endmembers, and thus, most Bi-LMMs focus mainly on the abundance estimation. This may lead to inaccurate estimation of endmembers and abundances for a given hyperspectral image. In this article, we propose a multiobjective endmember extraction (Bi-MoEE) method within the bilinear mixture paradigm, which considers each secondary reflection as a virtual endmember. Then, Bi-MoEE selects real and virtual endmembers from an extended spectral library consisting of a standard spectral library and their virtual products. By imposing some intuitive constraints, the solution space is greatly reduced, and the multipoint crossover and restricted bit-flip mutation operators are specially designed. Finally, Bi-MoEE can efficiently obtain a set of tradeoff solutions by minimizing the unmixing residuals and the number of selected endmembers, and automatically determine the optimal solution with multiobjective decision-making techniques. Compared with some advanced endmember extraction methods, the proposed Bi-MoEE does not need to know the number of real endmembers. In addition, the time efficiency of Bi-MoEE is mainly related to the image size and the algorithmic parameters, and has little to do with the size of spectral library, thus facilitating the practical implementation of Bi-MoEE with regard to the oversized spectral library. The experiments on synthetic and real data sets demonstrated the excellent performance of Bi-MoEE.

Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: Mathematical model and dragonfly algorithm based approach

Robotic assembly line outperforms labor assembly line in terms of efficiency and flexibility. This paper focuses on the mixed-model U-shaped robotic assembly line balancing and sequencing problem (MURALBSP). Different from most reported works, this paper includes the energy consideration. Two conflict objectives, i.e., energy consumption and makespan, are studied for energy saving and efficient production. To this end, a hybrid multi-objective dragonfly algorithm (HMODA) is proposed. First, the mathematical model of this bi-objective problem is formulated. Second, the basic dragonfly algorithm is improved to solve the problem. The specific encoding and decoding method is designed and the chaotic map is used to improve algorithm randomness. Besides, the solution update method is amended and multi-point crossover mechanism is employed. Finally, several multiple size benchmark problems are designed and comparisons are conducted, the sensitivity of decision variables is analyzed to provide managerial insights. The results suggest that HMODA is more efficient in solving the proposed problem than compared algorithms.

A secure image encryption scheme based on genetic operations and a new hybrid pseudo random number generator

This paper presents an encryption scheme based on genetic operations and a new hybrid pseudo random number generator (HPRNG). The new HPRNG is designed based on linear feedback shift register (LFSR), chaotic asymmetric tent map and chaotic logistic map. The scheme uses XOR and genetic operations (mutation, and multipoint crossover) to encrypt the image blocks. The first block of the plain image is encrypted with the help of a pseudo-random bit sequence generated by the HPRNG. The subsequent blocks are based on the previous cipher block and the XOR operator. The scheme can be extended to encrypt color images and text as well. The cipher images produced have very low correlation with their corresponding plain images and have high values of entropy, making it unpredictable and difficult to detect redundancies in the image pixel values. More over the scheme is compared with some existing schemes and found that the proposed scheme is comparatively secure and efficient.

PROBLEM SOLVING IN CROSS OVER LINE BALANCING USING HHO

The assembly line is a series of workflow manufacturing structures that are still common in large scale manufacturing of sustainable items. In the unbalanced tax system, more idle time is required to complete the task; A similar hanging problem arises when the tasks are not properly planned. This article proposes a technique to the line balance problem with crossover workstations. Different techniques were developed to resolve assembly line balance troubles. Existing methods for solving the assembly line balance problem can’t be implemented to included type issues. Using the Harris Hawks optimization, improve the performance of a network-based line balancing problem. The assembly line optimization is performed using the Harris Hawks optimization cycle multipoint crossover method to acquire actual posterity and the high-quality chromosome is chosen to determine the performance of the sequential construction system.

Optimalisasi Distribusi Harga Tiket Pesawat berdasarkan Kepadatan Rute Menggunakan Algoritma Genetika

The development of the aviation industry in Indonesia in the past decade has risen sharply. One of the impacts of the development of the aviation industry was the presence of a multilevel tariff concept. Where, the concept is the variation in ticket prices in one class with slightly different facilities such as the difference in penalty fees for making refunds and rebooking. The concept of multilevel rates is usually referred to as sub-class rates. One application of the sub-class tariffs in economic classes is divided into four types of sub-classes special promo sub-classes, promo sub-classes, then affordable sub-class and flexible sub-class. One optimization method of getting a combination that meets the requirements without having to try all possibilities is the Genetic Algorithm. The chromosomes built represent 10 subclasses on 9 routes so that they have 90 genes. The use of genetic algorithms originated from the generation of an initial population of 8 chromosomes with a length of 90 genes performed randomly, evaluation of the compatibility function was then selected using the Rank based fitness technique, crosses using Multi-Point Crossover, mutations with the Mutation Insertion technique. The system built was tested with two conditions each of eight tests with 100 generations. First, the test uses the mutation method of three subclass codes on four routes at a capacity of 150 seats, obtained the largest match value of Rp. 750,752,200 and the smallest Rp. 662,283,100. And testing with the mutation method of three subclass codes on eight routes of 150 seat capacity obtained the largest match value of Rp. 763,265,300 and the smallest Rp. 547,396,200. The results of testing the mutation method on eight routes resulted in a higher match value compared to the mutation method on four routes. The system has been implemented in software so that it can provide recommendations on the number of ticket passes distributed in the economic subclass.

Binary Coded Genetic Algorithm (BCGA) with Multi-Point Cross-Over for Magnetotelluric (MT) 1D Data Inversion

The magnetotelluric (MT) 1D modelling has been continuously receiving interest due to its effectiveness in obtaining overall subsurface resistivity image of an investigated area. The advances in computational resources allow increasing implementations of non-linear inversion using global search approach, such as Genetic Algorithm (GA). The genetic algorithm adopts the process of natural selection (survival for the fittest) and genetic transformation, i.e. selection, reproduction, mutation and population change, to solve the optimization problem. This paper discusses GA application in MT 1D modelling using binary coding representation with multi-point cross-over, i.e. one for every model parameter. The model parameters are resistivity and thickness of homogenously horizontal layers. The algorithm parameters need to be set to work properly, i.e. population size, number of genes, number of bits, crossover probability, mutation probability and number of generations. Despite binary coded GA (BCGA) drawbacks discussed in the literatures, we found that binary representation allows relatively extensive exploration of the search space. Test using synthetic data from three-layered synthetic models lead to satisfactory results, in terms of synthetic model recovery and data misfit comparable with the noise level contained the synthetic data.

Resource allocation based on hybrid genetic algorithm and particle swarm optimization for D2D multicast communications

Device-to-Device (D2D) multicast communication is a new technology of the fifth-generation networks (5G) for efficiently coping with the ever-increasing demand for content sharing among users. In fact, it enables direct communication between devices in proximity and improves spectral efficiency by reusing a licensed cellular spectrum. The existing related studies show that D2D communications increase network capacity and reduce latency. Nevertheless, the interference management should be carried out in a coordinated manner in order to realize the full potential of this technology and enable its integration into the cellular architecture. We consider the joint uplink subcarrier allocation and power control in D2D underlying cellular networks. In single rate multicast communications, the achieved data rate is greatly limited by the nodes with low channel quality. In this article, we formulate the resource allocation as a max–min optimization problem. Such an optimization problem is in general an NP-hard combinatorial problem and its solution typically requires searching enormous search trees. We propose a multicast schemes based on Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO). We implemented 8 different transfer functions combined with 2 update position strategies in order to assess the performance of BPSO. For the GA, we implemented Uniform and Multipoint crossover methods combined with Roulette Wheel and Tournament selectors. In addition to providing a deep understanding of the algorithm behavior, we present numerical results that demonstrates that GA outperforms BPSO in terms of minimum achieved data rate when the pressure of infeasibility is high.

An improved SFLA for flexible job shop scheduling problem considering energy consumption

In this paper, a multi-objective flexible job shop scheduling problem (MOFJSP) was studied systematically. A novel energy-saving scheduling model was established based on considering makespan and total energy consumption simultaneously. Different from previous studies, four types of energy consumption were considered in this model, including processing energy, idle energy, transport energy, and turn-on/off energy. In addition, a turn-off strategy is adopted for energy-saving. A modified shuffled frog-leaping algorithm (SFLA) was applied to solve this model. Moreover, operators of multi-point crossover and neighborhood search were both employed to obtain optimal solutions. Experiments were conducted to verify the performance of the SFLA compared with a non-dominated sorting genetic algorithm with blood variation (BVNSGA-II). The results show that this algorithm and strategy are very effective.

Comparison of GA-Shift Neighbourhood Mutation and GA-Pairs Exchange Mutation with Multi Cut Point Crossover in Solving RICH-VRP

This research was focused on a heterogeneous fleet of passenger ships to solve multi depot by using genetic algorithm (GA) to solve combinatorial problem i.e. vehicle routing problem (VRP). The objective of this study is to compare the roulette wheel selection, multi cut point crossover, and shift neighbourhood mutation with roulette wheel selection, multi cut point crossover, and pairs exchange mutation to minimize the sum of the fuel consumption travelled, the cost for violations of the ship draft and sea depth, and penalty cost for violations of the load factor; maximize number port of call; and maximize load factor. Problem solving in this study is how to generate feasible route combinations for rich VRP that meets all the requirements with optimum solution. Route generated by roulette wheel selection, multi cut point crossover, and shift neighbourhood mutation could decrease fuel consumption about 19.4350% compared to roulette wheel selection, multi cut point crossover, and pairs exchange mutation about 18.6738%.