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It was recently shown that quantum annealing can be used as an effective, fast subroutine in certain types of matrix factorization algorithms. The quantum annealing algorithm performed best for quick, approximate answers, but performance rapidly plateaued. In this paper, we utilize reverse annealing instead of forward annealing in the quantum annealing subroutine for nonnegative/binary matrix factorization problems. After an initial global search with forward annealing, reverse annealing performs a series of local searches that refine existing solutions. The combination of forward and reverse annealing significantly improves performance compared to forward annealing alone for all but the shortest run times.

As one of the manufacturing industries with high energy consumption and high pollution, sand casting is facing major challenges in green manufacturing. In order to balance production and green sustainable development, this paper puts forward man–machine dual resource constraint mechanism. In addition, a multi-objective flexible job shop scheduling problem model constrained by job transportation time and learning effect is constructed, and the goal is to minimize processing time energy consumption and noise. Subsequently, a hybrid discrete multi-objective imperial competition algorithm (HDMICA) is developed to solve the model. The global search mechanism based on the HDMICA improves two aspects: a new initialization method to improve the quality of the initial population, and the empire selection method based on Pareto non-dominated solution to balance the empire forces. Then, the improved simulated annealing algorithm is embedded in imperial competition algorithm (ICA), which overcomes the premature convergence problem of ICA. Therefore, four neighborhood structures are designed to help the algorithm jump out of the local optimal solution. Finally, an example is used to verify the feasibility of the proposed algorithm. By comparing with the original ICA and other four algorithms, the effectiveness of the proposed algorithm in the quality of the first frontier solution is verified.

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.

Crop diseases are a huge threat to food security, yet timely detection is a difficult task due to the absence of infrastructure in various regions of the world. In agriculture, the detection of disease in plants is complex because farmers must often evaluate whether the crop that are harvesting appears good enough. It is crucial to treat this seriously because it may result to major effects on plants, affecting product characteristics, quantity, or overall productivity. Plant illnesses produce outbreaks of disease on a systematic interval, resulting in large-scale fatalities and a substantial economic impact. Early and precise tools for diagnosing plant diseases are essential for robust plant production and for reducing both qualitative and quantitative losses in crop yield. Cutting-edge and creative data analysis technologies significantly aid in the accurate and precise identification of diseases. Among all the crops, tomato plants are widely grown and required in all parts of the world. Given all the above challenges, this study seeks to recognize tomato plant diseases in an accurate and timely manner. In this paper, a multi-objective hybrid fruit fly optimization algorithm that relies on simulated annealing optimized SVM is proposed to identify tomato plant diseases at an earlier stage in an accurate manner avoiding global optimization problems. The hybridization of simulated annealing with FOA helps in reducing the hyperparameter problems. The proposed methodology was tested and experimented extensively and the results enlightened that the proposed methodology achieved 91.1% accuracy and reliability and the experimental observations also indicated that the suggested method overcomes the drawbacks of the current algorithms. In addition, the operational efficiency of the proposed system was measured on statistical parameters like accuracy (91.1%), sensitivity (96.7%), precision (91.8%), specificity (91.2%), and F 1-score (94.5%). Also, a comparison analysis with existing algorithms like DT, RF, KNN, and K-means with SVM was also performed, and overall, it was concluded that proposed methodology is having high methodological approach for diagnosing crop diseases.

In the current noisy intermediate-scale quantum era, the limited number of high-fidelity qubits and restricted circuit depth pose significant challenges for large-scale quantum computation. Fortunately, distributed quantum computing (DQC) provides a feasible solution by dividing large quantum circuits into smaller subcircuits that can be executed on existing quantum processors. In this work, we propose a generalized model of circuit reconstruction (GMCR), which is capable of handling complex cutting patterns such as U-type structures to recover the output of the original circuit from the subcircuit results. In addition to the number of nonlocal gates and execution rounds, we introduce a new objective function in multi-objective simulated annealing (MOSA)-based cutting algorithm, the number of required SWAP operations in the subsequent mapping from logical qubits to physical qubits, which is used to satisfy the hardware connectivity constraints and to further decrease the complexity of quantum circuit compiling. We verified the GMCR model by cutting five circuits: encoding circuit for the Steane 7-qubit code, circuit of Shor’s algorithm, quantum supremacy circuit, quantum circuit of Bernstein–Vazirani algorithm, and circuit of approximate quantum Fourier transform. In the case of the Steane 7-qubit code, the number of reconstruction rounds was reduced from 337 to 156 under a fixed nonlocal gate count of two, while the number of SWAP operations was also reduced from 10 to 7 compared with the earlier MOSA-based algorithm. For the U-type subcircuits, using the GMCR model, the original results can be obtained, but cannot be obtained by the dynamic definition, approximate reconstruction algorithm, and fast reconstruction algorithm. This work plays an important role in implementing large-scale DQC, a typical application of future quantum Internet.

Manufacturing industries have become larger, diverse and the factors affecting a facility layout design have grown rapidly. Handling and evaluating these large set of criteria (factors) is difficult in designing and solving facility layout problems. These factors and uncertainties have a large impact on manufacturing time, manufacturing cost, product quality and delivery performance. In order to operate efficiently, these facilities should adapt to these variations over multiple time periods and this must be addressed while designing an optimal layout. This paper proposes a novel integrated framework by combining Big Data Analtics and Hybrid meta-heuristic approach to design an optimal facility layout under stochastic demand over multiple periods. Firstly, factors affecting a facility layout design are identified. The survey is conducted to generate data reflecting 3V’s of Big Data. Secondly, a reduced set of factors are obtained using Big Data Analytics. These reduced set of factors are considered to mathematically model a weighted aggregate objective for Multi-objective Stochastic Dynamic Facility Layout Problem (MO-SDFLP). Hybrid Meta-heuristic based on Firefly (FA) and Chaotic simulated annealing is used to solve the MO-SDFLP. To show the working methodology of proposed integrated framework an exemplary case is presented.

With the extensive application of 3D maps, acquiring high-quality images with unmanned aerial vehicles (UAVs) for precise 3D reconstruction has become a prominent topic of study. In this research, we proposed a coverage path planning method for UAVs to achieve full coverage of a target area and to collect high-resolution images while considering the overlap ratio of the collected images and energy consumption of clustered UAVs. The overlap ratio of the collected image set is guaranteed through a map decomposition method, which can ensure that the reconstruction results will not get affected by model breaking. In consideration of the small battery capacity of common commercial quadrotor UAVs, ray-scan-based area division was adopted to segment the target area, and near-optimized paths in subareas were calculated by a simulated annealing algorithm to find near-optimized paths, which can achieve balanced task assignment for UAV formations and minimum energy consumption for each UAV. The proposed system was validated through a site experiment and achieved a reduction in path length of approximately 12.6% compared to the traditional zigzag path.

The curve approximation problem is widely researched in CAD/CAM and geometric modelling. The problem consists in determining an approximating curve from a given sequence of points. The usual approach is the minimization of the discrepancy between the approximating curve and the given sequence of points. However, the minimization of just the discrepancy leads to the overfitting problem, in which the solution is not unique. A new approach is proposed to overcome this problem, in which the length of the approximating curve is used as a regularization increasing the algorithm stability. Another new proposal is the discrepancy determination, in which a method that has the best ratio between accuracy and processing time is proposed. A new simulated annealing (SA) approach is used to minimize the problem, in which the next candidate is determined by a probability distribution controlled by the crystallization factor. The crystallization factor is low for higher temperatures ensuring the exploration of the domain. The crystallization factor is high for lower temperatures, corresponding the refinement phase of the SA. The approximating curve is represented as a piecewise cubic Bézier curve, which is a sequence of several connected cubic Bézier curves. The piecewise Bézier curve supports a new proposed data structure that improves the proposed algorithm. A comparison is also made between the used single-objective SA and the AMOSA multi-objective SA. The results showed that the proposed single-objective SA finds a solution which is not dominated by the Pareto front determined by AMOSA. The results also showed that the regularization stabilized the algorithm, in which the increase in parameters does not lead to the overfitting problem. The proposed algorithm can process even complex curves with self-intersections and higher curvature.

Cryptographic algorithms are used to ensure confidentiality, integrity and authenticity of data in information systems. One of the important areas of modern cryptography is that of symmetric key ciphers. They convert the input plaintext into ciphertext, representing it as a random sequence of characters. S-boxes are designed to complicate the input–output relationship of the cipher. In other words, S-boxes introduce nonlinearity into the encryption process, complicating the use of different methods of cryptanalysis (linear, differential, statistical, correlation, etc.). In addition, S-boxes must be random. This property means that nonlinear substitution cannot be represented as simple algebraic constructions. Random S-boxes are designed to protect against algebraic methods of cryptanalysis. Thus, generation of random S-boxes is an important area of research directly related to the design of modern cryptographically strong symmetric ciphers. This problem has been solved in many related works, including some using the simulated annealing (SA) algorithm. Some works managed to generate 8-bit bijective S-boxes with a nonlinearity index of 104. However, this required enormous computational resources. This paper presents the results of our optimization of SA via various parameters. We were able to significantly reduce the computational complexity of substitution generation with SA. In addition, we also significantly increased the probability of generating the target S-boxes with a nonlinearity score of 104.

We propose an approach to find the optimal value of a convex semi-infinite program (SIP) that involves identifying a finite set of relevant constraints by solving a finite-dimensional global maximization problem. One of the major advantages of our approach is that it admits a plug-and-play module where any suitable global optimization algorithm can be employed to obtain the optimal value of the SIP. As an example, we propose a simulated annealing based algorithm which is useful especially when the constraint index set is high-dimensional. A proof of convergence of the algorithm is included, and the performance and accuracy of the algorithm itself are illustrated on several benchmark SIPs lifted from the literature.