scholarly journals Optimal Operating Depth Search for Active Towed Array Sonar using Simulated Annealing

2019 ◽  
Vol 69 (4) ◽  
pp. 415-419
Author(s):  
Sangkyum An ◽  
Keunhwa Lee ◽  
Woojae Seong
Keyword(s):  
Simulated Annealing ◽  
Optimal Solution ◽  
Optimal Operation ◽  
Computational Time ◽  
Performance Function ◽  
Active Sonar ◽  
Optimal Depth ◽  
Towed Array ◽  
Underwater Target ◽  
Ocean Environment

In an active towed array sonar, it is important to find the optimal operation depth. Generally, the optimal depth can be chosen via numerical simulations for all sonar depths and this imposes great burdens of time and cost.In this paper, an efficient approach is proposed to find the optimal depth using the optimisation technique. First, the sonar performance function is newly defined as a measure of how well the active sonar might perform. This function depends on the properties of the ocean environment and the positions of sonar and underwater target. Then, the simulated annealing to find an optimal solution for maximising sonar performance is used. The optimised depth agrees well with the depth obtained from direct searching for all depths of source and receiver combinations, but its computational time is largely reduced.

scholarly journals Benchmarking Quantum Annealing Against “Hard” Instances of the Bipartite Matching Problem

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Daniel Vert ◽  
Renaud Sirdey ◽  
Stéphane Louise
Keyword(s):  
Simulated Annealing ◽  
Polynomial Time ◽  
Optimal Solution ◽  
Quantum Computers ◽  
Quantum Annealing ◽  
Maximum Cardinality ◽  
Matching Problem ◽  
Bipartite Matching ◽  
Wave Device ◽  
D Wave

AbstractThis paper experimentally investigates the behavior of analog quantum computers as commercialized by D-Wave when confronted to instances of the maximum cardinality matching problem which is specifically designed to be hard to solve by means of simulated annealing. We benchmark a D-Wave “Washington” (2X) with 1098 operational qubits on various sizes of such instances and observe that for all but the most trivially small of these it fails to obtain an optimal solution. Thus, our results suggest that quantum annealing, at least as implemented in a D-Wave device, falls in the same pitfalls as simulated annealing and hence provides additional evidences suggesting that there exist polynomial-time problems that such a machine cannot solve efficiently to optimality. Additionally, we investigate the extent to which the qubits interconnection topologies explains these latter experimental results. In particular, we provide evidences that the sparsity of these topologies which, as such, lead to QUBO problems of artificially inflated sizes can partly explain the aforementioned disappointing observations. Therefore, this paper hints that denser interconnection topologies are necessary to unleash the potential of the quantum annealing approach.

scholarly journals Bio-Inspired Covert Active Sonar Strategy

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2436 ◽  
Author(s):  
Jiajia Jiang ◽  
Xianquan Wang ◽  
Fajie Duan ◽  
Chunyue Li ◽  
Xiao Fu ◽  
...  
Keyword(s):  
Marine Mammals ◽  
Estimation Method ◽  
Sperm Whale ◽  
Waveform Design ◽  
Velocity Estimation ◽  
Estimation Methods ◽  
Range Resolution ◽  
Active Sonar ◽  
Ocean Noise ◽  
Underwater Target

The covertness of the active sonar is a very important issue and the sonar signal waveform design problem was studied to improve covertness of the system. Many marine mammals produce call pulses for communication and echolocation, and existing interception systems normally classify these biological signals as ocean noise and filter them out. Based on this, a bio-inspired covert active sonar strategy was proposed. The true, rather than man-made sperm whale, call pulses were used to serve as sonar waveforms so as to ensure the camouflage ability of sonar waveforms. A range and velocity measurement combination (RVMC) was designed by using two true sperm whale call pulses which had excellent range resolution (RR) and large Doppler tolerance (DT). The range and velocity estimation methods were developed based on the RVMC. In the sonar receiver, the correlation technology was used to confirm the start and end time of sonar signals and their echoes, and then based on the developed range and velocity estimation method, the range and velocity of the underwater target were obtained. Then, the RVMC was embedded into the true sperm whale call-train to improve the camouflage ability of the sonar signal-train. Finally, experiment results were provided to verify the performance of the proposed method.

Locally optimal solution of robust ex-core detector response using constrained simulated annealing

2009 ◽  
Vol 239 (1) ◽  
pp. 51-57 ◽  
Author(s):  
Ho-Cheol Shin ◽  
Moon-Ghu Park ◽  
Sung-Tae Yang ◽  
Kyung-Ho Roh ◽  
Sang-Rae Moon ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimal Solution ◽  
Detector Response ◽  
Constrained Simulated Annealing ◽  
Locally Optimal Solution

scholarly journals Feature of Echo Envelope Fluctuation and Its Application in the Discrimination of Underwater Real Echo and Synthetic Echo

2018 ◽  
Vol 8 (8) ◽  
pp. 1329
Author(s):  
Yunfei Chen ◽  
Sheng Li ◽  
Bing Jia ◽  
Guijuan Li ◽  
Zhenshan Wang
Keyword(s):  
Physical Parameters ◽  
Scaled Model ◽  
Theoretical Simulation ◽  
Active Sonar ◽  
Fluctuation Intensity ◽  
Characterization Model ◽  
Novel Method ◽  
Underwater Target ◽  
Echo Features ◽  
Synthetic Target

Discriminating a real underwater target echo from a synthetic echo is a key challenge to identifying an underwater target. The structure of an echo envelope contains information which closely relates to the physical parameters of the underwater target, and the characterization and extraction of echo features are problematic issues for active sonar target classification. In this study, firstly, the high-frequency envelope fluctuation of a complex underwater target echo was analyzed, the envelope fluctuation was characterized by the envelope fluctuation intensity, and a characterization model was established. The features of a benchmark model echo were extracted and analyzed by theoretical simulation and sea testing of a scaled model, and the result shows that the envelope fluctuation intensity varies with carrier frequency and azimuth of incident signal, but the echo envelope fluctuation of the synthetic target echo does not present these features. Then, based on the characteristics of echo envelope fluctuation, a novel method was developed for active sonar discrimination of a real underwater target echo from the synthetic echo. Through a sea experiment, the real target echo and synthetic echo were classified by their different echo envelope fluctuations, and the feasibility of the method was verified.

scholarly journals A simulated annealing algorithm for vehicle scheduling problem

2017 ◽  
Vol 1 (Q4) ◽  
pp. 70-77
Author(s):  
Ha Thi Mai Phan
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Time Window ◽  
Fresh Concrete ◽  
Waiting Times ◽  
Optimal Solution ◽  
Production Scale ◽  
Construction Companies ◽  
Annealing Algorithm ◽  
Concrete Tank

As the construction activity has been growing, the companies that supply fresh concrete expand their production scale to meet their customers’ needs. The more customers, the longer queue tank trucks have to wait to pick up the fresh concrete. The customers are construction companies that have different construction works at the same time while the transportation time is only at night. They have to schedule efficiently the fleet of fresh concrete tank trucks during the night (turning the tank trucks a few turns) with constraints on the time window for the transfer of fresh concrete from the concrete company to the construction site as well as constraints on the waiting time for loading fresh concrete in the company. The scheduling for the fleet of construction company’s tank trucks will be modeled to minimize total transportation costs (fixed, variable) with estimated waiting times and tank truck’s turns several times during the night. The model of logistics problem is NP hard; Therefore, two algorithms are proposed to find the nearly optimal solution: heuristics and simulated annealing algorithm. The results will be compared and analyzed.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

scholarly journals ANALISA PERBANDINGAN METODE SIMULATED ANNEALING DAN LARGE NEIGHBORHOOD SEARCH UNTUK MEMECAHKAN MASALAH LOKASI DAN RUTE KENDARAAN DUA ESELON

2020 ◽  
Vol 4 (1) ◽  
pp. 35-46
Author(s):  
Winarno (Universitas Singaperbangsa Karawang) ◽  
A. A. N. Perwira Redi (Universitas Pertamina)
Keyword(s):  
Simulated Annealing ◽  
Computational Time ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Location Routing ◽  
Node Removal ◽  
Better Than

AbstractTwo-echelon location routing problem (2E-LRP) is a problem that considers distribution problem in a two-level / echelon transport system. The first echelon considers trips from a main depot to a set of selected satellite. The second echelon considers routes to serve customers from the selected satellite. This study proposes two metaheuristics algorithms to solve 2E-LRP: Simulated Annealing (SA) and Large Neighborhood Search (LNS) heuristics. The neighborhood / operator moves of both algorithms are modified specifically to solve 2E-LRP. The proposed SA uses swap, insert, and reverse operators. Meanwhile the proposed LNS uses four destructive operator (random route removal, worst removal, route removal, related node removal, not related node removal) and two constructive operator (greedy insertion and modived greedy insertion). Previously known dataset is used to test the performance of the both algorithms. Numerical experiment results show that SA performs better than LNS. The objective function value for SA and LNS are 176.125 and 181.478, respectively. Besides, the average computational time of SA and LNS are 119.02s and 352.17s, respectively.AbstrakPermasalahan penentuan lokasi fasilitas sekaligus rute kendaraan dengan mempertimbangkan sistem transportasi dua eselon juga dikenal dengan two-echelon location routing problem (2E-LRP) atau masalah lokasi dan rute kendaraan dua eselon (MLRKDE). Pada eselon pertama keputusan yang perlu diambil adalah penentuan lokasi fasilitas (diistilahkan satelit) dan rute kendaraan dari depo ke lokasi satelit terpilih. Pada eselon kedua dilakukan penentuan rute kendaraan dari satelit ke masing-masing pelanggan mempertimbangan jumlah permintaan dan kapasitas kendaraan. Dalam penelitian ini dikembangkan dua algoritma metaheuristik yaitu Simulated Annealing (SA) dan Large Neighborhood Search (LNS). Operator yang digunakan kedua algoritma tersebut didesain khusus untuk permasalahan MLRKDE. Algoritma SA menggunakan operator swap, insert, dan reverse. Algoritma LNS menggunakan operator perusakan (random route removal, worst removal, route removal, related node removal, dan not related node removal) dan perbaikan (greedy insertion dan modified greedy insertion). Benchmark data dari penelitian sebelumnya digunakan untuk menguji performa kedua algoritma tersebut. Hasil eksperimen menunjukkan bahwa performa algoritma SA lebih baik daripada LNS. Rata-rata nilai fungsi objektif dari SA dan LNS adalah 176.125 dan 181.478. Waktu rata-rata komputasi algoritma SA and LNS pada permasalahan ini adalah 119.02 dan 352.17 detik.

Multidisciplinary Optimization Design for Section Layout of Complex Umbilical Cables Based on Intelligent Algorithm

2015 ◽  
Author(s):  
Zhixun Yang ◽  
Jun Yan ◽  
Guojun Ma ◽  
Qingzhen Lu ◽  
Minggang Tang ◽  
...  
Keyword(s):  
Optimization Design ◽  
Oil And Gas ◽  
Power Transmission ◽  
Optimal Solution ◽  
Point Of View ◽  
Multidisciplinary Optimization ◽  
Intelligent Algorithm ◽  
Control Functions ◽  
Offshore Oil And Gas ◽  
Ocean Environment

Umbilical which links the top floater and the subsea devices provides control functions through electrical cables and hydraulic remote transmission. They are treated as the “lifeline” of the subsea production system for offshore oil and gas exploitation. During operation, umbilical needs to undertake self-weight and periodical load due to the ocean environment. Meanwhile, the heat during power transmission in electric cable is released to the umbilical body, which influences the mechanical properties and optical transmission in the cable. However, there are a number of components and many kinds of sectional arrangement for the umbilical. So the sectional design with multiple components needs to be solved as a multidisciplinary optimization problem. From the mechanical point of view, the umbilical structure should be designed with more compacted and symmetric layout to obtain even probability of resistance to loads and reduce structural stress to improve its fatigue performance. Concerning thermal effect, these units should be arranged to dissipate the heat easily to avoid the influence on the functional and structural components. In this paper, compactedness, symmetry and temperature distribution are quantified through introducing corresponding indices. Then multidisciplinary optimization framework is established. Particle Swarm Optimization (PSO) intelligent algorithm is adopted to carry out the optimization to obtain the optimal solution, which is far superior to the initial design. The optimization design strategy is proved to be effective and efficient by some numerical examples, which provides reference for design of umbilical cables.

Heuristic algorithm for optimal redundancy allocation in complex systems

2019 ◽  
Vol 25 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Sudhanshu Aggarwal
Keyword(s):  
Complex Systems ◽  
Heuristic Algorithm ◽  
Communication Systems ◽  
Heuristic Algorithms ◽  
Real Life ◽  
Optimal Solution ◽  
Systems Design ◽  
Computational Time ◽  
Coherent System ◽  
Content Type

PurposeThe purpose of this paper is to present an efficient heuristic algorithm based on the 3-neighborhood approach. In this paper, search is made from sides of both feasible and infeasible regions to find near-optimal solutions.Design/methodology/approachThe algorithm performs a series of selection and exchange operations in 3-neighborhood to see whether this exchange yields still an improved feasible solution or converges to a near-optimal solution in which case the algorithm stops.FindingsThe proposed algorithm has been tested on complex system structures which have been widely used. The results show that this 3-neighborhood approach not only can obtain various known solutions but also is computationally efficient for various complex systems.Research limitations/implicationsIn general, the proposed heuristic is applicable to any coherent system with no restrictions on constraint functions; however, to enforce convergence, inferior solutions might be included only when they are not being too far from the optimum.Practical implicationsIt is observed that the proposed heuristic is reasonably proficient in terms of various measures of performance and computational time.Social implicationsReliability optimization is very important in real life systems such as computer and communication systems, telecommunications, automobile, nuclear, defense systems, etc. It is an important issue prior to real life systems design.Originality/valueThe utilization of 3-neighborhood strategy seems to be encouraging as it efficiently enforces the convergence to a near-optimal solution; indeed, it attains quality solutions in less computational time in comparison to other existing heuristic algorithms.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

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