Zeitschriftenartikel: „Shor Algorithm“

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Monz, T., D. Nigg, E. A. Martinez, et al. "Realization of a scalable Shor algorithm." Science 351, no. 6277 (2016): 1068–70. http://dx.doi.org/10.1126/science.aad9480.

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Cherckesova, Larissa, Olga Safaryan, Pavel Razumov, Irina Pilipenko, Yuriy Ivanov, and Ivan Smirnov. "Speed improvement of the quantum factorization algorithm of P. Shor by upgrade its classical part." E3S Web of Conferences 224 (2020): 01016. http://dx.doi.org/10.1051/e3sconf/202022401016.

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This report discusses Shor’s quantum factorization algorithm and ρ–Pollard’s factorization algorithm. Shor’s quantum factorization algorithm consists of classical and quantum parts. In the classical part, it is proposed to use Euclidean algorithm, to find the greatest common divisor (GCD), but now exist large number of modern algorithms for finding GCD. Results of calculations of 8 algorithms were considered, among which algorithm with lowest execution rate of task was identified, which allowed the quantum algorithm as whole to work faster, which in turn provides greater potential for practical application of Shor’s quantum algorithm. Standard quantum Shor’s algorithm was upgraded by replacing the binary algorithm with iterative shift algorithm, canceling random number generation operation, using additive chain algorithm for raising to power. Both Shor’s algorithms (standard and upgraded) are distinguished by their high performance, which proves much faster and insignificant increase in time in implementation of data processing. In addition, it was possible to modernize Shor’s quantum algorithm in such way that its efficiency turned out to be higher than standard algorithm because classical part received an improvement, which allows an increase in speed by 12%.

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AVILA, M. A. "MINIMAL EXECUTION TIME OF SHOR'S ALGORITHM AT LOW TEMPERATURES." International Journal of Quantum Information 07, no. 01 (2009): 287–96. http://dx.doi.org/10.1142/s0219749909004475.

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The minimal time, T Shor , in which a one-way quantum computer can execute Shor's algorithm is derived. In the absence of an external magnetic field, this quantity diverges at very small temperatures. This result coincides with that of Anders et al. obtained simultaneously to ours but using thermodynamical arguments. Such divergence contradicts the common belief that it is possible to do quantum computation at low temperatures. It is shown that in the presence of a weak external magnetic field, T Shor becomes a quantized quantity which vanishes at zero temperature. Decoherence is not a problem because T Shor /τ dec < 10-9, where τdec is decoherence time.

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Lerner, E. Yu. "Prime witnesses in the Shor algorithm and the Miller-Rabin algorithm." Russian Mathematics 52, no. 12 (2008): 36–40. http://dx.doi.org/10.3103/s1066369x08120062.

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Hlukhov, V. "CAPACITIVE COMPLEXITY OF DETERMINING GCD IN THE SHOR S ALGORITHM." ELECTRICAL AND COMPUTER SYSTEMS 33, no. 108 (2020): 26–32. http://dx.doi.org/10.15276/eltecs.32.108.2020.3.

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The article analyzes the results of finding the period r of the function y = axmodM (a is a random number) which is used in the Shor's factorization algorithm for quantum computers. The module M is the product of two primes p and q. The article analyzes the solutions r obtained for various a, for which the capacitive complexity H of finding the greatest common divisor GCD(ar/2 + 1, M) is the least. A digital quantum computer is a classic processor and its digital quantum coprocessor. A digital quantum coprocessor with hundreds and thousands of digital qubits can be implemented in one programmable logic integrated circuit FPGA. In the Shor’s algorithm, the factorization problem of the number M reduces to the problem of determining the period r of the function y. It is known that GCD(ar/2 + 1, M) can be a divisor of the number M The task of the quantum coprocessor in implementing the Shor’s algorithm is to find the period r. After that it is necessary to find the GCD. Since for random a the problem of finding the period r has many solutions, these solutions can be compared by the value of one of the arguments when finding the GCD - the number ar / 2 . In this case, H = (rlog2a)/2 is taken for analysis. It approximately represents the bit depth of binary codes that a classic computer will have to process when determining the GCD. H can vary over a wide range from tens to thousands of bits even for small values of M. In this research the period r, which ensures the least complexity of the subsequent task of finding the GCD, is most often a solution for a = 3 and a = 2, but it can also occur often with other values of a. To clarify the revealed patterns, especially for large M, it is necessary to conduct additional research.

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Ekerå, Martin. "On post-processing in the quantum algorithm for computing short discrete logarithms." Designs, Codes and Cryptography 88, no. 11 (2020): 2313–35. http://dx.doi.org/10.1007/s10623-020-00783-2.

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Abstract We revisit the quantum algorithm for computing short discrete logarithms that was recently introduced by Ekerå and Håstad. By carefully analyzing the probability distribution induced by the algorithm, we show its success probability to be higher than previously reported. Inspired by our improved understanding of the distribution, we propose an improved post-processing algorithm that is considerably more efficient, enables better tradeoffs to be achieved, and requires fewer runs, than the original post-processing algorithm. To prove these claims, we construct a classical simulator for the quantum algorithm by sampling the probability distribution it induces for given logarithms. This simulator is in itself a key contribution. We use it to demonstrate that Ekerå–Håstad achieves an advantage over Shor, not only in each individual run, but also overall, when targeting cryptographically relevant instances of RSA and Diffie–Hellman with short exponents.

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Kiseliova, O. M., O. M. Prytomanova, and V. H. Padalko. "APPLICATION OF THE THEORY OF OPTIMAL SET PARTITIONING BEFORE BUILDING MULTIPLICATIVELY WEIGHTED VORONOI DIAGRAM WITH FUZZY PARAMETERS." EurasianUnionScientists 6, no. 2(71) (2020): 30–35. http://dx.doi.org/10.31618/esu.2413-9335.2020.6.71.615.

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An algorithm for constructing a multiplicatively weighted Voronoi diagram involving fuzzy parameters with the optimal location of a finite number of generator points in a limited set of n-dimensional Euclidean space 𝐸𝑛 has been suggested in the paper. The algorithm has been developed based on the synthesis of methods of solving the problems of optimal set partitioning theory involving neurofuzzy technologies modifications of N.Z. Shor 𝑟 -algorithm for solving nonsmooth optimization problems.

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Plesa, Mihail-Iulian, and Togan Mihai. "A New Quantum Encryption Scheme." Advanced Journal of Graduate Research 4, no. 1 (2018): 59–67. http://dx.doi.org/10.21467/ajgr.4.1.59-67.

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The model of quantum computation has advanced very quickly in the last years. This model brings with it an efficient algorithm for factoring, namely the Shor algorithm. This means that the public key infrastructure will soon be obsolete. In this paper we propose a new quantum cryptographic scheme which aims to replace the RSA algorithm from current public key infrastructures. We analyze the security of our scheme and also, we describe the implementation of the scheme using IBM Q SDK, qiskit. We run a number of experiments in order to build a proof of concept application that uses the proposed scheme.

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Ghisi, F., and S. V. Ulyanov. "The information role of entanglement and interference operators in Shor quantum algorithm gate dynamics." Journal of Modern Optics 47, no. 12 (2000): 2079–90. http://dx.doi.org/10.1080/09500340008235130.

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Ulyanov, F. Ghisi, S. V. "The information role of entanglement and interference operators in Shor quantum algorithm gate dynamics." Journal of Modern Optics 47, no. 12 (2000): 2079–90. http://dx.doi.org/10.1080/095003400419933.

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HESS, KARL, WALTER PHILIPP, and MANUEL ASCHWANDEN. "WHAT IS QUANTUM INFORMATION?" International Journal of Quantum Information 04, no. 04 (2006): 585–625. http://dx.doi.org/10.1142/s0219749906002080.

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The main purpose of this review is to deal with questions related to the nature of quantum information and particularly with quantum entanglement as an important component of quantum information and computing. We will not discuss here quantum computer algorithms, like the algorithm by Shor, or their advantages and disadvantages. We only cover the material that lies at the foundations of quantum information and computing and epistemological questions. We attempt to connect the famous debate between Einstein and Bohr on quantum entanglement to some of the latest work on qubits and quantum computing and to the mathematical theorems that form the basis of these discussions, particularly the theorem of Bell. We present a critical analysis of this material and hope that this will give the reader a better understanding of what can be said with certainty about the nature of quantum information.

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Téllez, Gustavo E., and Majid Sarrafzadeh. "On Rectilinear Distance-Preserving Trees." VLSI Design 7, no. 1 (1998): 15–30. http://dx.doi.org/10.1155/1998/26574.

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Given a set of terminals on the plane N={s,ν1,…,νn}, with a source terminal s, a Rectilinear Distance-Preserving Tree (RDPT) T(V, E) is defined as a tree rooted at s, connecting all terminals in N. An RDPT has the property that the length of every source to sink path is equal to the rectilinear distance between that source and sink. A Min- Cost Rectilinear Distance-Preserving Tree (MRDPT) minimizes the total wire length while maintaining minimal source to sink linear delay, making it suitable for high performance interconnect applications.This paper studies problems in the construction of RDPTs, including the following contributions. A new exact algorithm for a restricted version of the problem in one quadrant with O(n2) time complexity is proposed. A novel heuristic algorithm, which uses optimally solvable sub-problems, is proposed for the problem in a single quadrant. The average and worst-case time complexity for the proposed heuristic algorithm are O(n3/2) and O(n3), respectively. A 2-approximation of the quadrant merging problem is proposed. The proposed algorithm has time complexity O(α2T(n)+α3) for any constant α > 1, where T(n) is the time complexity of the solution of the RDPT problem on one quadrant. This result improves over the best previous quadrant merging solution which has O(n2T(n)+n3) time complexity.We test our algorithms on randomly uniform point sets and compare our heuristic RDPT construction against a Minimum Cost Rectilinear Steiner (MRST) tree approximation algorithm. Our results show that RDPTs are competitive with Steiner trees in total wire-length when the number of terminals is less than 32. This result makes RDPTs suitable for VLSI routing applications. We also compare our algorithm to the Rao-Shor RDPT approximation algorithm obtaining improvements of up to 10% in total wirelength. These comparisons show that the algorithms proposed herein produce promising results.

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Євсєєв, Сергій, Роман Корольов, Андрій Ткачов, and Анастасія Німченко. "DEVELOPMENT OF PROCEDURES FOR MODIFYING THE CIPHER GOST 28147." Advanced Information Systems 5, no. 2 (2021): 131–35. http://dx.doi.org/10.20998/2522-9052.2021.2.19.

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The entry of mankind into the era of high technologies, the rapid growth of computer technology contributes to the expansion of the range of electronic services. To ensure the security of confidential information, personal data, cryptographic systems of traditional cryptography (symmetric cryptosystems) and public key cryptography (asymmetric cryptosystems) are used. As a rule, the former provides security services, the latter provide key distribution. However, in the conditions of totalitarian surveillance in society by the special services of developed countries, cryptographic tabs are embedded in cryptographic algorithms, which, on the one hand, provide “quick” access for special services to confidential information, and on the other hand, allow intruders to break into the cryptosystem and obtain user data. The article proposes a modification of the well-known GOST 28147-89 algorithm, which ensures the "elimination" of possible crypto-bookmarks and an increase in crypto-resistance in the post-quantum period (the emergence of a full-scale quantum computer that allows hacking modern symmetric and asymmetric cryptosystems based on Grover and Shor algorithms). It is proposed to use the procedures for modifying the block-symmetric encryption algorithm (BSEA) GOST 28147-89 (2009, 2015) in OFB mode, which will make it possible to form a pseudo-random sequence based on dynamic changes in the S-box, and provide the required level of security.

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Zhang, Xingyi, Yunyun Niu, Linqiang Pan, and Mario J. Pérez-Jiménez. "Linear Time Solution to Prime Factorization by Tissue P Systems with Cell Division." International Journal of Natural Computing Research 2, no. 3 (2011): 49–60. http://dx.doi.org/10.4018/jncr.2011070105.

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Prime factorization is useful and crucial for public-key cryptography, and its application in public-key cryptography is possible only because prime factorization has been presumed to be difficult. A polynomial-time algorithm for prime factorization on a quantum computer was given by P. W. Shor in 1997. In this work, it is considered as a function problem, and in the framework of tissue P systems with cell division, a linear-time solution to prime factorization problem is given on biochemical computational devices – tissue P systems with cell division, instead of computational devices based on the laws of quantum physical.

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de Silva, Nadish. "Efficient quantum gate teleportation in higher dimensions." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2251 (2021): 20200865. http://dx.doi.org/10.1098/rspa.2020.0865.

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The Clifford hierarchy is a nested sequence of sets of quantum gates critical to achieving fault-tolerant quantum computation. Diagonal gates of the Clifford hierarchy and ‘nearly diagonal’ semi-Clifford gates are particularly important: they admit efficient gate teleportation protocols that implement these gates with fewer ancillary quantum resources such as magic states. Despite the practical importance of these sets of gates, many questions about their structure remain open; this is especially true in the higher-dimensional qudit setting. Our contribution is to leverage the discrete Stone–von Neumann theorem and the symplectic formalism of qudit stabilizer theory towards extending the results of Zeng et al . (2008) and Beigi & Shor (2010) to higher dimensions in a uniform manner. We further give a simple algorithm for recursively enumerating all gates of the Clifford hierarchy, a simple algorithm for recognizing and diagonalizing semi-Clifford gates, and a concise proof of the classification of the diagonal Clifford hierarchy gates due to Cui et al . (2016) for the single-qudit case. We generalize the efficient gate teleportation protocols of semi-Clifford gates to the qudit setting and prove that every third-level gate of one qudit (of any prime dimension) and of two qutrits can be implemented efficiently. Numerical evidence gathered via the aforementioned algorithms supports the conjecture that higher-level gates can be implemented efficiently.

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Junior Gabriel, Arome, Boniface Kayode Alese, Adebayo Olusola Adetunmbi, Olumide Sunday Adewale, and Oluwafemi Abimbola Sarumi. "Post-Quantum Crystography System for Secure Electronic Voting." Open Computer Science 9, no. 1 (2019): 292–98. http://dx.doi.org/10.1515/comp-2019-0018.

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AbstractSecurity (privacy, confidentiality and integrity) of pre-electoral, electoral and post electoral phases of the electioneering process is fundamental to the success of Electronic Voting (E-Voting) Systems. Crystography, which is the combination of cryptography and steganography could be a fitting ‘tool kit’ for enhancing the security of sensitive election-related information transmitted over public networks, thereby also ensuring free, fair and credible election/voting. Most of the existing secure e-voting systems are based on public key cryptographic schemes like RSA and Elliptic Curve Cryptography (ECC), whose security depends on the difficulty of solving Integer Factorization Problem (IFP) and Discrete Logarithm problem (DLP) respectively. However, techniques for solving IFP and DLP problems, improves continually. One of such is the quantum algorithm discovered by Peter Shor in 1994, which can solve both IFP and DLP problems in polynomial time. Consequently, the existence of quantum computers in the range of 1000 bits would spell doom to systems based on those problems. This paper presents the development of a new crystographic system that combines Post Quantum Cryptography with steganography to ensure that the security of e-voting is maintained both in classical computing era as well as post-quantum computing era. Our experiments’ results shows that our proposed system performed better than existing ones.

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Nie, Shu Zhi, Yan Hua Zhong, and Ming Hu. "Short-Time Traffic Flow Prediction Method Based on Universal Organic Computing Architecture." Advanced Materials Research 756-759 (September 2013): 2785–89. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2785.

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Designed a DNA-based genetic algorithm under the universal architecture of organic computing, combined particle swarm optimization algorithm, introduced a crossover operation for the particle location, can interfere with the particles speed, make inert particles escape the local optimum points, enhanced PSO algorithm's ability to get rid of local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and error analysis of experimental results showed that, the designed algorithms can accurately forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better, can be effectively applied to actual traffic engineering.

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Liu, Hong Ying. "Utilize Improved Particle Swarm to Predict Traffic Flow." Advanced Materials Research 756-759 (September 2013): 3744–48. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3744.

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Presented an improved particle swarm optimization algorithm, introduced a crossover operation for the particle location, interfered the particles speed, made inert particles escape the local optimum points, enhanced PSO algorithm's ability to break away from local extreme point. Utilized improved algorithms to train the RBF neural network models, predict short-time traffic flow of a region intelligent traffic control. Simulation and test results showed that, the improved algorithm can effetely forecast short-time traffic flow of the regional intelligent transportation control, forecasting effects is better can be effectively applied to actual traffic control.

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Andriansyah, Andriansyah, and Prima Denny Sentia. "PENENTUAN RUTE KENDARAAN PADA SISTEM DISTRIBUSI LOGISTIK PASCA BENCANA (STUDI KASUS)." Jurnal Manajemen Industri dan Logistik 2, no. 1 (2018): 79–89. http://dx.doi.org/10.30988/jmil.v2i1.28.

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The success indicators of disaster mitigation can be seen from the disaster logistics system. Effective and efficient distribution network can make a good disaster logistics system. The problem that related to the design of this network is the vehicle routing problem. The objective is determined optimal route of relief distribution from warehouse to victims with minimum time duration. The problem is solved by branch and bound, insertion heuristic, and local search algorithms. The results obtained by branch and bound and local search algorithm are optimal global. Time duration of vehicle using these algoritm is 1.0562 hours. However, computation time using branch and bound algorithm is very long until 22 hours while local search algorithm only takes 60 seconds. The insertion heuristic algorithm also produces a good solution. Time duration of vehicle using this algoritm is 1,1030 hours. This solution is local optimal, but the computation time is very short, only 0.001 seconds.

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Huang, Xiabao, Zailin Guan, and Lixi Yang. "An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem." Advances in Mechanical Engineering 10, no. 9 (2018): 168781401880144. http://dx.doi.org/10.1177/1687814018801442.

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Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multi-objective flexible job-shop scheduling problem.

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Chen, Zhenpeng, Yuanjie Zheng, Xiaojie Li, et al. "Interactive Trimap Generation for Digital Matting Based on Single-Sample Learning." Electronics 9, no. 4 (2020): 659. http://dx.doi.org/10.3390/electronics9040659.

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Image matting refers to the task of estimating the foreground of images, which is an important problem in image processing. Recently, trimap generation has attracted considerable attention because designing a trimap for every image is labor-intensive. In this paper, a two-step algorithm is proposed to generate trimaps. To use the proposed algorithm, users must only provide some clicks (foreground clicks and background clicks), which are employed as the input to generate a binary mask. One-shot learning technique achieves remarkable progress on semantic segmentation, we extend this technique to perform the binary mask prediction task. The mask is further used to predict the trimap using image dilation. Extensive experiments were performed to evaluate the proposed algorithm. Experimental results show that the trimaps generated using the proposed algorithm are visually similar to the user-annotated ones. Comparing with the interactive matting algorithms, the proposed algoritm is less labor-intensive than trimap-based matting algorithm and achieved more accuate results than scribble-based matting algorithm.

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Litinskaia, Evgeniia L., Pavel A. Rudenko, Kirill V. Pozhar, and Nikolai A. Bazaev. "Validation of Short-Term Blood Glucose Prediction Algorithms." International Journal of Pharma Medicine and Biological Sciences 8, no. 2 (2019): 34–39. http://dx.doi.org/10.18178/ijpmbs.8.2.34-39.

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Liu, Mei Hong, and Xiong Feng Peng. "Improved Adaptive Genetic Algorithms for Job Shop Scheduling Problems." Advanced Materials Research 97-101 (March 2010): 2473–76. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.2473.

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In this paper, the adaptability of the genetic algorithm (GA) is considered. Two improved adaptive genetic algorithms (AGA) which are called Ch-AGA and Th-AGA for short are proposed based on the previous AGA. The crossover probability and the mutation probability of the Ch-AGA and the Th-AGA are non-linear changed between some a certain region, and adopted the mathematical function of chx and thx respectively. The two improved adaptive genetic algorithms are used to solve the classical job shop scheduling problems and the results indicate that the algorithms are more effective and more efficient than previous AGA, and should be used in practical applications.

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Vairam, Senthil, and V. Selladurai. "Parallel Machine Shop Scheduling Using Memetic Algorithm." Applied Mechanics and Materials 573 (June 2014): 362–67. http://dx.doi.org/10.4028/www.scientific.net/amm.573.362.

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Parallel machine shop scheduling problem can be stated as finding a schedule for a general task graph to execute on a customed flow so that the schedule length can be minimized. Parallel Flow Shop Scheduling with a case study has been . In this study we present an effective memetic algorithm to solve the problem. Also evaluating the performance of two algorithms (genetic algorithm and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better and quality solutions and hence it is very efficient . KEY WORDS: Hybrid Flow Shop Scheduling, Multiprocessor, Memetic algorithm.

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Kim, Chyon Hae, Hiroshi Tsujino, and Shigeki Sugano. "Rapid Short-Time Path Planning for Phase Space." Journal of Robotics and Mechatronics 23, no. 2 (2011): 271–80. http://dx.doi.org/10.20965/jrm.2011.p0271.

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This paper addresses optimal motion for general machines. Approximation for optimal motion requires a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that consists of path searching and pruning algorithms. The pruning algorithmis based on our analysis of state resemblance in general phase space. To confirm precision, calculation cost, optimality and applicability of the proposed algorithm, we conducted several shortest time path planning experiments for the dynamic models of double inverted pendulums. Precision to reach the goal states of the pendulums was better than other algorithms. Calculation cost was 58 times faster at least. We could tune optimality of proposed algorithm via resolution parameters. A positive correlation between optimality and resolutions was confirmed. Applicability was confirmed in a torque based position and velocity feedback control simulation. As a result of this simulation, the double inverted pendulums tracked planned motion under noise while keeping within torque limitations.

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Pongchairerks, Pisut. "Forward VNS, Reverse VNS, and Multi-VNS Algorithms for Job-Shop Scheduling Problem." Modelling and Simulation in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/5071654.

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This paper proposes a number of forward VNS and reverse VNS algorithms for job-shop scheduling problem. The forward VNS algorithms are the variable neighborhood search algorithms applied to the original problem (i.e., the problem instance with the original precedence constraints). The reverse VNS algorithms are the variable neighborhood search algorithms applied to the reversed problem (i.e., the problem instance with the reversed precedence constraints). This paper also proposes a multi-VNS algorithm which assigns an identical initial solution-representing permutation to the selected VNS algorithms, runs these VNS algorithms, and then uses the best solution among the final solutions of all selected VNS algorithms as its final result. The aim of the multi-VNS algorithm is to utilize each single initial solution-representing permutation most efficiently and thus receive its best result in return.

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Raborn, Anthony W., Walter L. Leite, and Katerina M. Marcoulides. "A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development." Educational and Psychological Measurement 80, no. 5 (2020): 910–31. http://dx.doi.org/10.1177/0013164420906600.

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This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and relationship to external variables. Using a Monte Carlo simulation study, this study compared existing implementations of the ant colony optimization, Tabu search, and genetic algorithm to select short forms of scales, as well as a new implementation of the simulated annealing algorithm. Selection of short forms of scales with unidimensional, multidimensional, and bifactor structure were evaluated, with and without model misspecification and/or an external variable. The results showed that when the confirmatory factor analysis model of the full form of the scale was correctly specified or had only minor misspecification, the four algorithms produced short forms with good psychometric qualities that maintained the desired factor structure of the full scale. Major model misspecification resulted in worse performance for all algorithms, but including an external variable only had minor effects on results. The simulated annealing algorithm showed the best overall performance as well as robustness to model misspecification, while the genetic algorithm produced short forms with worse fit than the other algorithms under conditions with model misspecification.

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Park, Rae-Jun, Kyung-Bin Song, and Bo-Sung Kwon. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning." Energies 13, no. 10 (2020): 2640. http://dx.doi.org/10.3390/en13102640.

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Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

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Akram Abdulrazzaq, Atheer, Nur’Aini Abdul Rashid, and Ahmed Majid Taha. "The Enhanced Hybrid Algorithm for the AbdulRazzaq and Berry-Ravindran Algorithms." International Journal of Engineering & Technology 7, no. 3 (2018): 1709. http://dx.doi.org/10.14419/ijet.v7i3.12436.

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Exact string matching is one of the critical issues in the field of computer science. This study proposed a hybrid string matching algorithm called E- AbdulRazzaq. This algorithm used the best properties of two original algorithms; AbdulRazzaq and Berry-Ravindran Algorithms. The proposed algorithm showed an efficient performance in the number of attempts and number of character comparison when compared the original and recent to the standard algorithms. The proposed algorithm was applied in several types of databases, which are DNA sequences, Protein sequences, XML structures, Pitch characters, English texts, and Source codes. The Pitch database was the best match for E-AbdulRazzaq with the number of attempts involving long and short patterns, while the DNA database was the worst match. No data is specified as the best or worst with the E-AbdulRazzaq algorithm in terms of the character comparisons. The E-AbdulRazzaq algorithms ranked first in most databases when using short and long patterns, in terms of number of attempts and character comparisons.

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Bao, Yun, Liping Zheng, and Hua Jiang. "An Efficient Hybrid Based on HS and GA Solving Blocking Flow Shop Scheduling Problems." Advanced Materials Research 479-481 (February 2012): 1893–96. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1893.

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This paper presents an efficient hybrid algorithms (EHA) based on harmony search (HS) algorithms and genetic algorithm (GA) for solving blocking flow shop scheduling problem. An improved GA is used to get better results. The computational result shows that EHA is not only better than GA , but also better than HS algorithm.

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Qiu, Dong Wei, and Shan Shan Wan. "Research on Algorithms Performance about JSP Scheduling." Advanced Materials Research 457-458 (January 2012): 20–25. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.20.

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Three typical intelligent evolutionary algorithms are applied on Job Shop scheduling problem which are Quantum algorithm, Genetic Algorithm and Population Based Incremental Learning algorithm. They three algorithms have some common features in computation, encoding strategy and probability application, but with the different problems and different scale sizes of the same problem they show different performance. In this paper we take JSP as example to test their performance difference and analyze their applicability. Two benchmark Job Shop problems are used to fulfill the comparison. The results denote that Quantum algorithm is good in a great quantity of solution individual, GA is excellent in stability and PBIL had good performance in accuracy. The research also makes a reliable instruction on the application or combination of the three algorithms.

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Nightingale, P., I. P. Gent, C. Jefferson, and I. Miguel. "Short and Long Supports for Constraint Propagation." Journal of Artificial Intelligence Research 46 (January 17, 2013): 1–45. http://dx.doi.org/10.1613/jair.3749.

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Special-purpose constraint propagation algorithms frequently make implicit use of short supports -- by examining a subset of the variables, they can infer support (a justification that a variable-value pair may still form part of an assignment that satisfies the constraint) for all other variables and values and save substantial work -- but short supports have not been studied in their own right. The two main contributions of this paper are the identification of short supports as important for constraint propagation, and the introduction of HaggisGAC, an efficient and effective general purpose propagation algorithm for exploiting short supports. Given the complexity of HaggisGAC, we present it as an optimised version of a simpler algorithm ShortGAC. Although experiments demonstrate the efficiency of ShortGAC compared with other general-purpose propagation algorithms where a compact set of short supports is available, we show theoretically and experimentally that HaggisGAC is even better. We also find that HaggisGAC performs better than GAC-Schema on full-length supports. We also introduce a variant algorithm HaggisGAC-Stable, which is adapted to avoid work on backtracking and in some cases can be faster and have significant reductions in memory use. All the proposed algorithms are excellent for propagating disjunctions of constraints. In all experiments with disjunctions we found our algorithms to be faster than Constructive Or and GAC-Schema by at least an order of magnitude, and up to three orders of magnitude.

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Yang, Yan Li, and Wei Wei Ke. "Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm." Advanced Materials Research 411 (November 2011): 588–91. http://dx.doi.org/10.4028/www.scientific.net/amr.411.588.

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An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

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Huang, Ying Jie, Xi Fan Yao, Dong Yuan Ge, and Yong Xiang Li. "Entropy-Enhanced Genetic Algorithm with Tabu Search for Job Shop Scheduling Problems." Advanced Materials Research 590 (November 2012): 557–62. http://dx.doi.org/10.4028/www.scientific.net/amr.590.557.

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By combining Genetic algorithm with Tabu search algorithm and adjusting crossover rate and mutation rate based on information entropy, a hybrid genetic algorithm was proposed for larger-scale job shop scheduling problems, and the benchmark instances were used to verify the algorithm with simulation. Simulation results show that the proposed algorithm can solve larger-scale job shop scheduling problems, and it has obvious advantages over traditional scheduling algorithms.

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Cao, Xian Zhou, and Zhen He Yang. "A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling." Applied Mechanics and Materials 55-57 (May 2011): 1789–93. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1789.

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In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.

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Wandishin, Matthew S., Michael E. Baldwin, Steven L. Mullen, and John V. Cortinas. "Short-Range Ensemble Forecasts of Precipitation Type." Weather and Forecasting 20, no. 4 (2005): 609–26. http://dx.doi.org/10.1175/waf871.1.

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Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

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Dardani, Ian, and Gerard F. Jones. "Algorithms for optimization of branching gravity-driven water networks." Drinking Water Engineering and Science 11, no. 1 (2018): 67–85. http://dx.doi.org/10.5194/dwes-11-67-2018.

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Abstract. The design of a water network involves the selection of pipe diameters that satisfy pressure and flow requirements while considering cost. A variety of design approaches can be used to optimize for hydraulic performance or reduce costs. To help designers select an appropriate approach in the context of gravity-driven water networks (GDWNs), this work assesses three cost-minimization algorithms on six moderate-scale GDWN test cases. Two algorithms, a backtracking algorithm and a genetic algorithm, use a set of discrete pipe diameters, while a new calculus-based algorithm produces a continuous-diameter solution which is mapped onto a discrete-diameter set. The backtracking algorithm finds the global optimum for all but the largest of cases tested, for which its long runtime makes it an infeasible option. The calculus-based algorithm's discrete-diameter solution produced slightly higher-cost results but was more scalable to larger network cases. Furthermore, the new calculus-based algorithm's continuous-diameter and mapped solutions provided lower and upper bounds, respectively, on the discrete-diameter global optimum cost, where the mapped solutions were typically within one diameter size of the global optimum. The genetic algorithm produced solutions even closer to the global optimum with consistently short run times, although slightly higher solution costs were seen for the larger network cases tested. The results of this study highlight the advantages and weaknesses of each GDWN design method including closeness to the global optimum, the ability to prune the solution space of infeasible and suboptimal candidates without missing the global optimum, and algorithm run time. We also extend an existing closed-form model of Jones (2011) to include minor losses and a more comprehensive two-part cost model, which realistically applies to pipe sizes that span a broad range typical of GDWNs of interest in this work, and for smooth and commercial steel roughness values.

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Łatuszyński, Krzysztof, and Jeffrey S. Rosenthal. "The Containment Condition and Adapfail Algorithms." Journal of Applied Probability 51, no. 04 (2014): 1189–95. http://dx.doi.org/10.1017/s0021900200012055.

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This short note investigates convergence of adaptive Markov chain Monte Carlo algorithms, i.e. algorithms which modify the Markov chain update probabilities on the fly. We focus on the containment condition introduced Roberts and Rosenthal (2007). We show that if the containment condition is not satisfied, then the algorithm will perform very poorly. Specifically, with positive probability, the adaptive algorithm will be asymptotically less efficient then any nonadaptive ergodic MCMC algorithm. We call such algorithms AdapFail, and conclude that they should not be used.

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Łatuszyński, Krzysztof, and Jeffrey S. Rosenthal. "The Containment Condition and Adapfail Algorithms." Journal of Applied Probability 51, no. 4 (2014): 1189–95. http://dx.doi.org/10.1239/jap/1421763335.

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This short note investigates convergence of adaptive Markov chain Monte Carlo algorithms, i.e. algorithms which modify the Markov chain update probabilities on the fly. We focus on the containment condition introduced Roberts and Rosenthal (2007). We show that if the containment condition is not satisfied, then the algorithm will perform very poorly. Specifically, with positive probability, the adaptive algorithm will be asymptotically less efficient then any nonadaptive ergodic MCMC algorithm. We call such algorithms AdapFail, and conclude that they should not be used.

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Ghosh, Smarajit, Manvir Kaur, Suman Bhullar, and Vinod Karar. "Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems." Energies 12, no. 3 (2019): 551. http://dx.doi.org/10.3390/en12030551.

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The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

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Dai, Min, Dun Bing Tang, Kun Zheng, and Qi Xiang Cai. "An Improved Heuristic Algorithm for a Hybrid Flow-Shop Scheduling." Applied Mechanics and Materials 333-335 (July 2013): 1414–17. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1414.

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This paper presents an improved metaheuristic algorithm to minimize the makespan in a hybrid flow-shop scheduling (HFS) with non-identical parallel machines. First, a mathematical model for an HFS problem is introduced. Second, an improved simulated annealing algorithm (ISAA) which is inspired from a hormone modulation mechanism is presented to retrofit speed and accuracy of the algorithm. Finally, the computer simulation demonstrates the good quality of the proposed procedure, and it outperforms several other algorithms.

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Kamal Amjad, Muhammad, Shahid Ikramullah Butt, and Naveed Anjum. "Improved Genetic Algorithm Integrated with Scheduling Rules for Flexible Job Shop Scheduling Problems." E3S Web of Conferences 243 (2021): 02010. http://dx.doi.org/10.1051/e3sconf/202124302010.

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This paper presents optimization of makespan for Flexible Job Shop Scheduling Problems (FJSSP) using an Improved Genetic Algorithm integrated with Rules (IGAR). Machine assignment is done by Genetic Algorithm (GA) and operation selection is done using priority rules. Improvements in GA include a new technique of adaptive probabilities and a new forced mutation technique that positively ensures the generation of new chromosome. The scheduling part also proposed an improved scheduling rule in addition to four standard rules. The algorithm is tested against two well-known benchmark data set and results are compared with various algorithms. Comparison shows that IGAR finds known global optima in most of the cases and produces improved results as compared to other algorithms.

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Varmazyar, Mohsen, and Nasser Salmasi. "Minimizing the Number of Tardy Jobs in Flow Shop Sequence Dependent Setup Times Scheduling Problem." Applied Mechanics and Materials 110-116 (October 2011): 4063–69. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.4063.

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This paper investigates permutation flow shop scheduling problems with sequence-dependent setup times with minimizing the number of tardy jobs as criterion (Fm|prmu,Sijk|∑Uj). Since the proposed research problem has been proven to be NP-hard, three different meta heuristic algorithms based on tabu search (TS) has been proposed to solve the problem. These three algorithms are different based on their tabu list characteristics. In order to evaluate the performance of the proposed TS algorithms, test problems in different ranges are generated to find the best algorithm. The comparison shows that the TS algorithm which its tabu list keeps track of the slots that the jobs are assigned has a better performance than the other algorithms

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Panggabean, Jonas Franky R. "Hybrid Ant Colony Optimization-Genetics Algorithm to Minimize Makespan Flow Shop Scheduling." International Journal of Engineering & Technology 7, no. 2.2 (2018): 40. http://dx.doi.org/10.14419/ijet.v7i2.2.11868.

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Flow shop scheduling is a scheduling model in which the job to be processed entirely flows in the same product direction / path. In other words, jobs have routing work together. Scheduling problems often arise if there is n jobs to be processed on the machine m, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. In research of Zini, H and ElBernoussi, S. (2016) NEH Heuristic and Stochastic Greedy Heuristic (SG) algorithms. This paper presents modified harmony search (HS) for flow shop scheduling problems with the aim of minimizing the maximum completion time of all jobs (makespan). To validate the proposed algorithm this computational test was performed using a sample dataset of 60 from the Taillard Benchmark. The HS algorithm is compared with two constructive heuristics of the literature namely the NEH heuristic and stochastic greedy heuristic (SG). The experimental results were obtained on average for the dataset size of 20 x 5 to 50 x 10, that the ACO-GA algorithm has a smaller makespan than the other two algorithms, but for large-size datasets the ACO-GA algorithm has a greater makespan of both algorithms with difference of 1.4 units of time.

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Tian, Ai-Qing, Shu-Chuan Chu, Jeng-Shyang Pan, Huanqing Cui, and Wei-Min Zheng. "A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station." Sustainability 12, no. 3 (2020): 767. http://dx.doi.org/10.3390/su12030767.

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Pigeon-inspired optimization (PIO) is a new type of intelligent algorithm. It is proposed that the algorithm simulates the movement of pigeons going home. In this paper, a new pigeon herding algorithm called compact pigeon-inspired optimization (CPIO) is proposed. The challenging task for multiple algorithms is not only combining operations, but also constraining existing devices. The proposed algorithm aims to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. A real-valued prototype vector performs probability and statistical calculations, and then generates optimal candidate solutions for CPIO optimization algorithms. The CPIO algorithm was used to evaluate a variety of continuous multi-model functions and the largest model of hydropower short-term generation. The experimental results show that the proposed algorithm is a more effective way to produce competitive results in the case of limited memory devices.

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Wang, Fang, Yun Qing Rao, and Qiu Hua Tang. "A Hybrid Intelligence Algorithm for No-Wait Flow Shop Scheduling." Advanced Materials Research 712-715 (June 2013): 2447–51. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2447.

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Constraint simplified mixed integer programming model was presented based on time transformation mechanism of no-wait flow shop. And a hybrid intelligence algorithm which combines the advantages of heuristic algorithm and neighborhood search algorithm was proposed. The initial population was generated by Johnson method, NEH method, Rajendran Method, Dannerbring method, and heuristic rules. The crossover and mutation operators in each generation were introduce neighborhood search (NS) and tabu search (TS). And the optimal individual was reserved in each generation. We compared the new hybrid intelligence algorithm (abbreviation H&NSGA ) with the algorithm blending heuristic and GA (NSGA), the algorithm blending neighborhood search and GA (HAGA), GA with the optimal individual reserved, and results show that the results and stability of solutions based on H&NSGA are better than the other three algorithms.

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Ramya, G., and M. Chandrasekaran. "Solving Job Shop Scheduling Problem Based on Employee Availability Constraint." Applied Mechanics and Materials 376 (August 2013): 197–206. http://dx.doi.org/10.4028/www.scientific.net/amm.376.197.

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Manufacturing System is enabled with an excellent knowledge on production plan, proper scheduling of machinery process, employee timetabling and labor costs. Heuristic algorithms are developed to bring optimized results in stipulated time with respect to optimum schedule. This article deals with minimizing the maximum completion time (makespan) based on job scheduling and minimization of labor costs based on employee workload with Shuffled Frog Leaping Algorithm and Sheep Flock Heredity Model Algorithm. The labor costs minimization and makespan which is to find a schedule that satisfies the organizations rules, employees preferences, due date and customers. The formulation of assigning workload for employees is concerned with assigning number of employees into a given set of shifts over a fixed period of time and week task. The main problem attempts to minimize labor costs based on performance criteria and assigning the loads equally among all employees. Several local search methods and heuristics algorithms has been proposed in many research on Job shop scheduling. The Results are compared with other heuristics in terms of makespan, idle time and Labor costs the Shuffled Frog Leaping algorithm performs result oriented than other Heuristics Algorithm.

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Wen, Ming Yue, Yi Zhang, Fang Jun Hu, and Zheng Liu. "Solving the Job-Shop Scheduling Problem Based on Cellular Genetic Algorithm." Applied Mechanics and Materials 433-435 (October 2013): 639–44. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.639.

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Cellular genetic algorithm (cGA) is a subclass of genetic algorithm (GA) in which the population diversity and exploration are enhanced thanks to the existence of small overlapped neighborhoods. Such a kind of structured algorithms is specially well suited for complex problems. Shop scheduling problem is a kind of problem with practical significance, and it belongs to a combinational optimization problem called NP-hard problem. In this paper we establish the model of job-shop problem (JSP) and solve the job-shop scheduling problem with cGA and traditional genetic algorithms (sGA).From the experimental results and analysis, we find cGA has better search efficiency and convergence performance than sGA.

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Zhang, Guo Hui. "Solving the Flexible Job Shop Scheduling Problem Based on Memetic Algorithm." Advanced Materials Research 544 (June 2012): 1–5. http://dx.doi.org/10.4028/www.scientific.net/amr.544.1.

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Flexible job shop scheduling problem (FJSP) is a well known NP-hard combinatorial optimization problem due to its very large search space and many constraint between jobs and machines. Evolutionary algorithms are the most widely used techniques in solving FJSP. Memetic algorithm is a hybrid evolutionary algorithm that combines the local search strategy and global search strategy. In this paper, an effective memetic algorithm is proposed to solve the FJSP. In the proposed algorithm, variable neighborhood search is adopted as local search algorithm. The neighborhood functions is generated by exchanging and inserting the key operations which belong to the critical path. The optimization objective is to minimize makespan. The experimental results obtained from proposed algorithm show that the proposed algorithm is very efficient and effective for all tested problems.

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Utama, Dana Marsetiya, Dian Setiya Widodo, Muhammad Faisal Ibrahim, and Shanty Kusuma Dewi. "An effective hybrid ant lion algorithm to minimize mean tardiness on permutation flow shop scheduling problem." International Journal of Advances in Intelligent Informatics 6, no. 1 (2020): 23. http://dx.doi.org/10.26555/ijain.v6i1.385.

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This article aimed to develop an improved Ant Lion algorithm. The objective function was to minimize the mean tardiness on the flow shop scheduling problem with a focus on the permutation flow shop problem (PFSP). The Hybrid Ant Lion Optimization Algorithm (HALO) with local strategy was proposed, and from the total search of the agent, the NEH-EDD algorithm was applied. Moreover, the diversity of the nominee schedule was improved through the use of swap mutation, flip, and slide to determine the best solution in each iteration. Finally, the HALO was compared with some algorithms, while some numerical experiments were used to show the performances of the proposed algorithms. It is important to note that comparative analysis has been previously conducted using the nine variations of the PFSSP problem, and the HALO obtained was compared to other algorithms based on numerical experiments.

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