Metaheuristic Optimization of a Discrete Array of Radiant Heaters

2006 ◽  
Vol 128 (10) ◽  
pp. 1031-1040 ◽  
Author(s):  
Jason M. Porter ◽  
Marvin E. Larsen ◽  
J. Wesley Barnes ◽  
John R. Howell
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Search Algorithm ◽  
Radiation Heat Transfer ◽  
Computation Time ◽  
Optimization Methods ◽  
Optimization Approach ◽  
Metaheuristic Optimization ◽  
Design Variables ◽  
Radiant Heater

The design of radiant enclosures is an active area of research in radiation heat transfer. When design variables are discrete such as for radiant heater arrays with on-off control of individual heaters, current methods of design optimization fail. This paper reports the development of a metaheuristic thermal radiation optimization approach. Two metaheuristic optimization methods are explored: simulated annealing and tabu search. Both approaches are applied to a combinatorial radiant enclosure design problem. Configuration factors are used to develop a dynamic neighborhood for the tabu search algorithm. Results are presented from the combinatorial optimization problem. Tabu search with a problem specific dynamic neighborhood definition is shown to find better solutions than the benchmark simulated annealing approach in less computation time.

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.

scholarly journals Global optimization of default phases for parallel transmit coils for ultra-high-field cardiac MRI

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255341
Author(s):  
Maxim Terekhov ◽  
Ibrahim A. Elabyad ◽  
Laura M. Schreiber
Keyword(s):  
Cardiac Mri ◽  
Computation Time ◽  
Optimization Methods ◽  
Fine Tuning ◽  
Optimization Approach ◽  
Brute Force ◽  
Global Extremum ◽  
High Magnetic Field Strength ◽  
Brute Force Method ◽  
Key Steps

The development of novel multiple-element transmit-receive arrays is an essential factor for improving B1+ field homogeneity in cardiac MRI at ultra-high magnetic field strength (B0 > = 7.0T). One of the key steps in the design and fine-tuning of such arrays during the development process is finding the default driving phases for individual coil elements providing the best possible homogeneity of the combined B1+-field that is achievable without (or before) subject-specific B1+-adjustment in the scanner. This task is often solved by time-consuming (brute-force) or by limited efficiency optimization methods. In this work, we propose a robust technique to find phase vectors providing optimization of the B1-homogeneity in the default setup of multiple-element transceiver arrays. The key point of the described method is the pre-selection of starting vectors for the iterative solver-based search to maximize the probability of finding a global extremum for a cost function optimizing the homogeneity of a shaped B1+-field. This strategy allows for (i) drastic reduction of the computation time in comparison to a brute-force method and (ii) finding phase vectors providing a combined B1+-field with homogeneity characteristics superior to the one provided by the random-multi-start optimization approach. The method was efficiently used for optimizing the default phase settings in the in-house-built 8Tx/16Rx arrays designed for cMRI in pigs at 7T.

Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem

2013 ◽  
pp. 52-70
Author(s):  
Tabitha James ◽  
Cesar Rego
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Assignment Problem ◽  
Search Algorithm ◽  
Quadratic Assignment Problem ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Path Relinking ◽  
Quadratic Assignment ◽  
Swarm Optimization

This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem (QAP) in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its applicability to many domains. The algorithm presented in this study employs path relinking as a solution combination method incorporating a multistart tabu search algorithm as an improvement method. The resulting algorithm has interesting similarities and contrasts with particle swarm optimization methods. Computational testing indicates that this algorithm produces results that rival the best QAP algorithms. The authors additionally conduct an analysis disclosing how different strategies prove more or less effective depending on the landscapes of the problems to which they are applied. This analysis lays a foundation for developing more effective future QAP algorithms, both for methods based on path relinking and tabu search, and for hybrids of such methods with related processes found in particle swarm optimization.

Concept of Neighborhood Degree and its Application to Switching Plural Optimization Methods in Scheduling

2010 ◽  
Vol 14 (1) ◽  
pp. 21-27
Author(s):  
Fangyan Dong ◽  
◽  
Kewei Chen ◽  
Kaoru Hirota ◽  
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Vehicle Routing ◽  
Optimization Methods ◽  
Optimization Process ◽  
Scheduling Problems ◽  
Solution Quality ◽  
Practical Applications ◽  
Benchmark Data

A concept of neighborhood degree is proposed to evaluate the quality of solutions to scheduling problems such as vehicle routing, scheduling, and dispatching problems. It is possible to apply it to the optimization process of scheduling problems in order to switch between various optimization methods by considering convergence speed and solution quality. In the experiments on TSP benchmark data, two optimization methods, i.e., tabu search and simulated annealing, are switched effectively by observing the variation of the neighborhood degree. Directions for Practical applications are also mentioned.

scholarly journals Risk-Based Path Planning Optimization Methods for UAVs Over Inhabited Areas

2015 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Risk Assessment ◽  
Path Planning ◽  
Network Optimization ◽  
Computation Time ◽  
Optimization Methods ◽  
Flight Path ◽  
Low Risk ◽  
Optimization Approach ◽  
Local Approach ◽  
Solution Quality

Operating unmanned aerial vehicles (UAVs) over inhabited areas requires mitigating the risk to persons on the ground. Because the risk depends upon the flight path, UAV operators need approaches (techniques) that can find low-risk flight paths between the mission’s start and finish points. In some areas, the flight paths with the lowest risk are excessively long and indirect because the least-populated areas are too remote. Thus, UAV operators are concerned about the tradeoff between risk and flight time. Although there exist approaches for assessing the risks associated with UAV operations, existing risk-based path planning approaches have considered other risk measures (besides the risk to persons on the ground) or simplified the risk assessment calculation. This paper presents a risk assessment technique and bi-objective optimization methods to find low-risk and time (flight path) solutions and computational experiments to evaluate the relative performance of the methods (their computation time and solution quality). The methods were a network optimization approach that constructed a graph for the problem and used that to generate initial solutions that were then improved by a local approach and a greedy approach and a fourth method that did not use the network solutions. The approaches that improved the solutions generated by the network optimization step performed better than the optimization approach that did not use the network solutions.

Does Complex Metaheuristic Out-Perform Simple Hill-Climbing for Optimization Problems? A Simulation Evaluation

2013 ◽  
Vol 748 ◽  
pp. 666-669 ◽  
Author(s):  
Xing Wen Zhang
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Noise ◽  
Computation Time ◽  
Hill Climbing ◽  
Supply Chain Optimization ◽  
Solution Quality ◽  
Metaheuristic Methods

In this paper we compare the performance of metaheuristic methods, namely simulated annealing and Tabu Search, against simple hill climbing heuristic on a supply chain optimization problem. The benchmark problem we consider is the retailer replenishment optimization problem for a retailer selling multiple products. Computation and simulation results demonstrate that simulated annealing and Tabu search improve solution quality. However, the performance improvement is less in simulations with random noise. Lastly, simulated annealing appears to be more robust than Tabu search, and the results justify its extra implementation effort and computation time when compared against hill climbing.

scholarly journals Optimal Network Destruction Strategy with Heterogeneous Cost under Cascading Failure Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fang Yang ◽  
Tao Ma ◽  
Tao Wu ◽  
Hong Shan ◽  
Chunsheng Liu
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Network Performance ◽  
Search Algorithm ◽  
Cascading Failure ◽  
Tabu Search Algorithm ◽  
Failure Model ◽  
The Cost ◽  
Node Removal ◽  
The Impact

By studying an attacker’s strategy, defenders can better understand their own weaknesses and prepare a response to potential threats in advance. Recent studies on complex networks using the cascading failure model have revealed that removing critical nodes in the network will seriously threaten network security due to the cascading effect. The conventional strategy is to maximize the declining network performance by removing as few nodes as possible, but this ignores the difference in node removal costs and the impact of the removal order on network performance. Having considered all factors, including the cost heterogeneity and removal order of nodes, this paper proposes a destruction strategy that maximizes the declining network performance under a constraint based on the removal costs. First, we propose a heterogeneous cost model to describe the removal cost of each node. A hybrid directed simulated annealing and tabu search algorithm is then devised to determine the optimal sequence of nodes for removal. To speed up the search efficiency of the simulated annealing algorithm, this paper proposes an innovative directed disturbance strategy based on the average cost. After each annealing iteration, the tabu search algorithm is used to adjust the order of node removal. Finally, the effectiveness and convergence of the proposed algorithm are evaluated through extensive experiments on simulated and real networks. As the cost heterogeneity increases, we find that the impact of low-cost nodes on network security becomes larger.

A New Hybrid Binary-Real Coded Cuckoo Search and Tabu Search Algorithm for Solving the Unit-Commitment Problem

2021 ◽  
Vol 10 (2) ◽  
pp. 104-119
Author(s):  
Amel Terki ◽  
Hamid Boubertakh
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Unit Commitment Problem ◽  
Local Optima

This paper proposes a new intelligent optimization approach to deal with the unit commitment (UC) problem by finding the optimal on/off states strategy of the units under the system constraints. The proposed method is a hybridization of the cuckoo search (CS) and the tabu search (TS) optimization techniques. The former is distinguished by its efficient global exploration mechanism, namely the levy flights, and the latter is a successful local search method. For this sake, a binary code is used for the status of units in the scheduled time horizon, and a real code is used to determine the generated power by the committed units. The proposed hybrid CS and TS (CS-TS) algorithm is used to solve the UC problem such that the CS guarantees the exploration of the whole search space, while the TS algorithm deals with the local search in order to avoid the premature convergence and prevent from trapping into local optima. The proposed method is applied to the IEEE standard systems of different scales ranging from 10 to 100 units. The results show clearly that the proposed method gives better quality solutions than the existing methods.

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