Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications

This paper represents how typical advanced engineering design can be structured using a set of parameters and objective functions corresponding to the nature of the problem. The set of parameters can be in different types, including integer, real, cyclic, combinatorial, interval, etc. Similarly, the objective function can be presented in various types including integer (discrete), float, and interval. The simulated annealing with crystallization heuristic can deal with all these combinations of parameters and objective functions when the crystallization heuristic presents a sensibility for real parameters. Herein, simulated annealing with the crystallization heuristic is enhanced by combining Bates and Gaussian distributions and by incorporating feedback strategies to emphasize exploration or refinement, or a combination of the two. The problems that are studied include solving an electrical impedance tomography problem with float parameters and a partially evaluated objective function represented by an interval requiring the solution of 32 sparse linear systems defined by the finite element method, as well as an airplane design problem with several parameters and constraints used to reduce the explored domain. The combination of the proposed feedback strategies and simulated annealing with the crystallization heuristic is compared with existing simulated annealing algorithms and their benchmark results are shown. The enhanced simulated annealing approach proposed herein showed better results for the majority of the studied cases.

Development of Particle Swarm Optimization and Simulated Annealing Algorithms to Solve Vehicle Routing Problems with Drones

Vehicle Routing Problem with Drone (VRPD) is a problem of determining the number of routes for delivery of goods from the depot to a number of customers using trucks and drones. Drones are an alternative delivery tool besides trucks, each truck can be equipped with a support drone. Drones can be used to make a delivery while the truck is making others. By combining a truck and a drone, the truck can act as a tool for drone launch and landing so that the drones can reach long distances from the depot. The purpose of this problem is to minimize the cost of sending goods by trucks and drones. In this study, the Particle Swarm Optimization (PSO) and the Simulated Annealing (SA) are proposed to solve these problems. The Route Drone algorithm are used to help change the structure of the PSO and SA solutions into a VRPD solution. The proposed algorithm has been applied to 24 different scenarios ranging from 6 customers to 100 customers. The PSO and SA algorithms are able to find solutions that are close to optimal. The SA is able to find a better solution than the PSO.

A novel and robust technique for identifying prestress forces in prestressed concrete beams using generic finite elements and simulated annealing algorithms

This work presents a novel technique for estimating the prestressing forces in simply supported beams with axial prestress force. The technique is based on the use of generic finite elements for modeling the beam and experimental time-domain response to simultaneously identify axial forces and generic parameters. Parameter updating is accomplished using a Simulated Annealing algorithm implemented for the solution of the prestress force identification problem. The effectiveness of the method was assessed in numerical simulations and was further verified on an experimental prestressed concrete beam. The results show that the inclusion of generic elements allows the identification of the force to be achieved even in the presence of errors in model parameters, thus eliminating the restraints of previous approaches.

Engineering Reflective Metasurfaces with Ising Hamiltonian and Quantum Annealing

<div><div><div><p>We present a novel and flexible method to optimize the phase response of reflective metasurfaces towards a desired scattering profile. The scattering power is expressed as a spin-chain Hamiltonian using the radar cross section formalism. For metasurfaces reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective metasurfaces. Even though the work is focused on the phase modulation of metasurfaces, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces that are mod- ulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks.</p></div></div></div>

A simulated annealing-based algorithm for selecting balanced samples

Balanced sampling is a random method for sample selection, the use of which is preferable when auxiliary information is available for all units of a population. However, implementing balanced sampling can be a challenging task, and this is due in part to the computational efforts required and the necessity to respect balancing constraints and inclusion probabilities. In the present paper, a new algorithm for selecting balanced samples is proposed. This method is inspired by simulated annealing algorithms, as a balanced sample selection can be interpreted as an optimization problem. A set of simulation experiments and an example using real data shows the efficiency and the accuracy of the proposed algorithm.

Sweeping quantum annealing algorithm for constrained optimization problems

As a typical quantum computing algorithm, quantum annealing is widely used in the optimization of glass-like problems to find the best solution. However, the optimization problems in constrained complex systems usually involve topological structures, and the performance of the quantum annealing algorithm is still largely unknown. Here, we take an Ising system as a typical example with local constraints accompanied by intrinsic topological properties that can be implemented on quantum computing platforms such as the D-wave machine, and study the effectiveness of the quantum annealing algorithm in its optimization and compare it with that of the thermal annealing. We find that although conventional quantum annealing is difficult for the optimization of constrained topological problems, a generalized algorithm --- the sweeping quantum annealing method --- can be designed and solve the problem with better efficiency than both conventional quantum and thermal annealing algorithms. The sweeping quantum annealing algorithm, therefore, opens up a promising avenue for quantum computing of constrained problems and can be readily employed on the optimizations in quantum material design, engineering, and even social sciences.

Engineering Reflective Metasurfaces with Ising Hamiltonian and Quantum Annealing

<div><div><div><p>We present a novel and flexible method to optimize the phase response of reflective metasurfaces towards a desired scattering profile. The scattering power is expressed as a spin-chain Hamiltonian using the radar cross section formalism. For metasurfaces reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective metasurfaces. Even though the work is focused on the phase modulation of metasurfaces, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces that are mod- ulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks.</p></div></div></div>

Engineering Reflective Metasurfaces with Ising Hamiltonian and Quantum Annealing

<div><div><div><p>We present a novel and flexible method to optimize the phase response of reflective metasurfaces towards a desired scattering profile. The scattering power is expressed as a spin-chain Hamiltonian using the radar cross section formalism. For metasurfaces reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective metasurfaces. Even though the work is focused on the phase modulation of metasurfaces, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces that are mod- ulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks.</p></div></div></div>

Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing

With the constant growth of requirements to the quality of infocommunication services, special attention is paid to the management of information transfer in wireless self-organizing networks. The clustering algorithm based on the Motley signal propagation model has been improved, resulting in cluster formation based on the criterion of shortest distance and maximum signal power value. It is shown that the use of the improved clustering algorithm compared to its classical version is more efficient for the route search process. Ant and simulated annealing algorithms are presented to perform route search in a wireless sensor network based on the value of the quality of service parameter. A comprehensive routing method based on finding the global extremum of an ordered random search with node addition/removal is proposed by using the presented ant and simulated annealing algorithms. It is shown that the integration of the proposed clustering and routing solutions can reduce the route search duration up to two times.