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To find a good set of compiler options for a particular CPU and software is actually a difficult task. Some tool based on genetic algorithm like AcovEA exists, and it requires to compile and run each program many times as to find optimized compiler options. If each program execution time is very long, then the process is rather time consuming. In this paper, a new tool, named Analysis of Compiler...
Design of both a suitable window shape and appropriate weights in weighted median filters is one of important problems. Hitherto, unsupervised design methods of the filters by using simulated annealing (SA) or genetic algorithm (GA) have been proposed for texture images corrupted by impulse noise. These techniques estimate the optimal window shape and the optimal filter weights separately, and they...
Several strategies have been proposed to provide quality solutions to the unit commitment problem and increase the potential saving in the power system operation. These include deterministic and stochastic search algorithms. One of the limitations of deterministic approaches is, they suffer from the curse of dimensionality when dealing with the modern power system with large number of generators....
Many real-time systems are in fact isochronal, where both early and late responses are harmful to the system or lead to lower quality of service. Task scheduling problems in real-time systems proved that are NP-hard problems. Therefore, heuristic search strategies can be applied to these problems. In this paper, a simulated annealing algorithm is proposed for static task scheduling in non-overloaded...
A pragmatic hybrid genetic algorithm named parallel adaptive genetic simulated annealing (PAGSA) is developed. The proposed hybrid approach combines the merits of genetic algorithm (GA) with simulated annealing (SA) to construct a more efficient genetic simulated annealing (GSA) algorithm for global search, while the iterative hill climbing (IHC) method is used as a local search technique to incorporate...
This paper proposes a combinational optimization algorithm extremal optimization (EO) for protein structure alignment based on the contact map overlap (CMO) model. EO is a meta-heuristic algorithm, as genetic algorithm and simulated annealing, but with a local fitness introduced to guide the improvement of the optimization. By exploiting similarity matrix between two contact maps, the results demonstrate...
A multiobjective optimal service restoration methodology for shipboard power system (SPS) using a simulated annealing genetic algorithm is presented. All the multiple objective functions related to restoration problem for SPS are formulated as fuzzy sets. An analytic hierarchy process (AHP) is adopted to determine the weighted factors of each objective functions which is more convenient to be apprehended...
In this paper, a novel classification approach is presented. This approach uses fuzzy if-then rules for classification task and employs a hybrid optimization method to improve the accuracy and comprehensibility of obtained outcome. The mentioned optimization method has been formulated by simulated annealing and genetic algorithm. In fact, the genetic operators have been used as perturb functions at...
A direct numerical approach for space vehicle trajectory optimization was proposed. The direct transcription with Simpson quadrature was used for discretization of constrained optimal control problem. The genetic algorithm with simulated annealing penalty function was used for global search. The application to space vehicle orbit transfer optimization indicated that this method could improve the sensitive...
The issue of the guarantee quality of service (QOS) for users can be provided by the advanced reservation. The advanced reservation is a kind of mechanism that can provide the ability to allocate resources for users based on the agreement upon the needs of quality service and increase of the number of accepted users' requests in grid system. Scheduling and advanced reservation of the resources in...
A type of genetic simulated annealing algorithms (GSAAs) is presented, which is used to optimize the parameters of proportional-integral-derivative (PID) controllers. This approach combines the merits of genetic algorithms (GAs) and simulated annealing algorithms (SAAs). By integrating the global search ability of GA with the local search ability of SAA, the search ability of GSAA is much stronger...
Since the BP neural network algorithm has some unavoidable disadvantages, such as slowly converging speed and easily running into local minimum, the genetic algorithm and simulated annealing algorithm with the overall search capability have been put forward to optimize authority value and threshold value of BP nerve network. In this paper, a new neural network model which is optimized by genetic algorithm...
Cutting stock problem is to save material and optimize the utilizing of the resources, which is being widely used in product designing, manufacturing and applying. It is very complicated and difficult in terms of the calculation theory. This paper analyzed the Genetic Annealing Algorithm and applied it in the area of stock cutting optimization of fiberboard furniture. This paper presented the key...
Optimization of Non-Deterministic Polynomial hard (NP-hard) problems of non-trivial sizes is done using heuristic approach. Until now, simulated annealing (SA), genetic algorithm (GA) and Hopfield neural network (HNN) were individually used for solving the standard cell placement (SCP) problem. Now, in this paper we discuss systems based on hybridizing HNN, SA and GA. We begin with HNN & GA hybrid...
A large project in a company is often divided to several subtasks, which would be assigned to different people with variant abilities to the same task. So whether the tasks are scheduled properly would determine the quality or the efficiency of team collaboration. A hybrid particle swarm optimization (PSO) algorithm is putted forward. Subtasks are disassembled from the project by using the task tree...
The BP neural network algorithm can not guarantee an error plane as the overall minimum in the training process. It may have a number of local minimum rather than the optimal solution to the issue. To solve this issue, a new genetic algorithm of self-adaptive annealing is designed on the basis of standard genetic algorithm, combined with algorithms for global optimization of simulated annealing to...
This paper presents a hybrid heuristic algorithm for large scale multiple depots vehicle routing problem (MDVRP) in relief work which combined genetic algorithm with ant systems and simulated annealing algorithm. The main idea of this new algorithm is a feedback loop. Using the best result of genetic algorithm to improve ant colony system and feed the best solution of ant system back to genetic algorithm...
Selection of timetables for a transit system is a vital aspect of the schedule problem. An optimal model of timetables is presented for regional bus scheduling problem. Its objective is to optimize timetables in such a way that the transfer time of passengers at the transfer nodes is minimized while the operational constraints such as the traffic demand, departure time and maximum (minimum) headway...
In order to realize the stability and the accuracy of particle inversion algorithm, on the basis of present commonly used algorithm, one kind of particle size distributed stochastic inversion algorithm is proposed. The algorithm combines the advantage of the annealing evolution algorithm and the genetic algorithm and has the stronger global convergence. The algorithm has ability to distinguish the...
Frequency and phase combined modulation waveform has been considered as an important technology that provides significantly improved LPI performance and compression ratio in Radar system. In this paper, genetic simulated annealing algorithm(GASA) has been applied to the design and optimization of the combined modulation waveform. On the basis of genetic algorithm(GA), GASA introduces annealing process...
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