Improved trust-region gradient-search algorithm for accelerated optimization of wideband antenna input characteristics

2018 ◽  
Vol 29 (4) ◽  
pp. e21576
Author(s):  
Slawomir Koziel
Keyword(s):  
Search Algorithm ◽  
Trust Region ◽  
Gradient Search ◽  
Wideband Antenna ◽  
Antenna Input

Optimum Synthesis of Rigid Mechanisms Using Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) With Neighborhood Search

2007 ◽  
Author(s):  
Ahmad Smaili ◽  
Mazen Hassanieh ◽  
Bachir Chaaya ◽  
Fawzan Al Fares
Keyword(s):  
Quantum Computing ◽  
Search Algorithm ◽  
Random Search ◽  
Quantum Gates ◽  
Neighborhood Search ◽  
Gradient Search ◽  
Quantum Bits ◽  
Optimum Synthesis ◽  
Evolution Algorithm ◽  
Optimization Schemes

A modified real coded quantum-inspired evolution algorithm (MRQIEA) is herein presented for optimum synthesis of planar rigid body mechanisms (RBMs). The MRQIEA employs elements of quantum computing such as quantum bits, registers, and quantum gates, neighborhood search engine, and gradient search to form a random search algorithm for solution optimization of a wide class of problems. A brief overview of the quantum computing elements and their adaptation to the optimization algorithm is first presented. The algorithm is then adapted to the synthesis problem of RBMs. Finally, the algorithm is demonstrated and compared to other search methods by way of three examples, including two benchmark examples that have been used in the literature to assess the performance of other optimization schemes.

Thermal morphing anisogrid smart space structures part 2: Ranking of geometric parameter importance, trust region optimization, and performance evaluation

2017 ◽  
Vol 24 (13) ◽  
pp. 2873-2893 ◽  
Author(s):  
Austin A Phoenix ◽  
Jeff Borggaard ◽  
Pablo A Tarazaga
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Search Algorithm ◽  
Thermal Strain ◽  
Computational Cost ◽  
Trust Region ◽  
Minimum Cost ◽  
Space Mission ◽  
Model Parameters

As future space mission structures are required to achieve more with scarcer resources, new structural configurations and modeling capabilities will be needed to meet the next generation space structural challenges. A paradigm shift is required away from the current structures that are static, heavy, and stiff, to innovative lightweight structures that meet requirements by intelligently adapting to the environment. As the complexity of these intelligent structures increases, the computational cost of the modeling and optimization efforts become increasingly demanding. Novel methods that identify and reduce the number of parameters to only those most critical considerably reduce these complex problems, allowing highly iterative evaluations and in-depth optimization efforts to be computationally feasible. This parameter ranking methodology will be demonstrated on the optimization of the thermal morphing anisogrid boom. The proposed novel morphing structure provides high precision morphing through the use of thermal strain as the sole actuation mechanism. The morphing concept uses the helical members in the anisogrid structure to provide complex constrained actuations that can achieve the six degree of freedom morphing capability. This structure provides a unique potential to develop an integrated structural morphing system, where the adaptive morphing capability is integrated directly into the primary structure. To identify parameters of interest, the Q-DEIM model reduction algorithm is implemented to rank the model parameters based on their impact on the morphing performance. This parameter ranking method provides insight into the system and enables the optimal allocation of computational and engineering resources to the most critical areas of the system for optimization. The methodology, in conjunction with a singular value decomposition (SVD), provides a ranking and identifies parameters of relative importance. The SVD is used to truncate the nine parameters problem at two locations, generating a five parameter optimization problem and a three parameter optimization problem. To evaluate the ranking, a parameter sweep in conjunction with a simple minimum cost function search algorithm will compare all 120 five parameter ranking orders to the Q-DEIM ranking. This reduced parameter set significantly reduces the parameter complexity and the computational cost of the model optimization. This paper will present the methodology to define the resulting performance of the optimal thermal morphing anisogrid structure, minimum morphing control, and the systems frequency response capability as a function of available power.

A Tabu-Gradient Search-Based Optimization Technique for Synthesis of Mechanisms

2004 ◽  
Author(s):  
Ahmad Smaili ◽  
Naji Atallah
Keyword(s):  
Tabu Search ◽  
Global Minimum ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Gradient Search ◽  
Optimum Synthesis ◽  
Timing Task

Mechanism synthesis requires the use of optimization methods to obtain approximate solution whenever the desired number of positions the mechanism is required to traverse exceeds a few (five in a 4R linkage). Deterministic gradient-based methods are usually impractical when used alone because they move in the direction of local minima. Random search methods on the other hand have a better chance of converging to a global minimum. This paper presents a tabu-gradient search based method for optimum synthesis of planar mechanisms. Using recency-based short-term memory strategy, tabu-search is initially used to find a solution near global minimum, followed by a gradient search to move the solution ever closer to the global minimum. A brief review of tabu search method is presented. Then, tabu-gradient search algorithm is applied to synthesize a four-bar mechanism for a 10-point path generation with prescribed timing task. As expected, Tabu-gradient base search resulted in a better solution with less number of iterations and shorter run-time.

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