An improved sine cosine algorithm (abbreviated Msa) is presented to overcome the disadvantages of the sine cosine algorithm (SCA), such as its low accuracy, premature convergence, and slow local convergence. We change...
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An improved sine cosine algorithm (abbreviated Msa) is presented to overcome the disadvantages of the sine cosine algorithm (SCA), such as its low accuracy, premature convergence, and slow local convergence. We changed the method for setting the conversion parameters of the SCA algorithm from a linear decline into a nonlinear decline in order to optimize the timing of global exploration and local exploration. In order to improve the convergence accuracy and to increase the convergence speed of the SCA, an inertia weight is introduced in the position update equation. Ten high-dimensional complex benchmark functions are simulated with five improved SCA algorithms, under the same particle numbers and maximum iteration times. According to our experiments, the Msa algorithm is not only much better than two of the improved SCA algorithms in optimi-zation accuracy but also better than the other three improved SCA algorithms in terms of stability.
This paper introduces a Single-Item Lot-sizing and Scheduling Problem with Multiple Warehouses (SILSP-MW). In this problem, the inventory deteriorates over time, depending on the warehouse conditions, so multiple ware...
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This paper introduces a Single-Item Lot-sizing and Scheduling Problem with Multiple Warehouses (SILSP-MW). In this problem, the inventory deteriorates over time, depending on the warehouse conditions, so multiple warehouses with different technologies are considered in this study. Each warehouse has a specified deterioration rate and holding cost. The purpose of the SILSP-MW is to determine production periods and quantities, and to select the appropriate warehouse to hold the inventory in each period, such that specified demand in each period is being satisfied while the total cost is minimized. We shall present a Mixed-Integer Linear Programming (MILP) formulation to model the problem. Moreover, a Simulated Annealing (sa) algorithm will be presented to solve this problem. We will evaluate the performance of the algorithm by computational experiments with small- and medium-sized examples. In addition, a full factorial design is developed to investigate the effect of the model parameters on the proposed sa algorithm. (C) 2013 Sharif University of Technology. All rights reserved.
This paper highlights the separation of cyclohexane/tert-butanol from industrial effluent by heterogeneous pressure-swing azeotropic distillation based on the properties of pressure-sensitive distillation boundaries a...
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This paper highlights the separation of cyclohexane/tert-butanol from industrial effluent by heterogeneous pressure-swing azeotropic distillation based on the properties of pressure-sensitive distillation boundaries and natural liquid-liquid separation. There is no excess component in the separating process. The optimal parameters of the conventional process are obtained by using the simulated annealing algorithm. Additional heat pump and heat integration strategies are introduced into the process with the optimal operation parameter to save more energy. Evaluation indicators, such as total annual cost (TAC), total energy consumption (TEC), gas emission, and exergy destruction, are used to assess the potential of economic and environment friendly processes. The heat pump assisted HADPSD process employing auxiliary reboilers and heat integration is the optimal energy -saving process, exhibiting decreases of 41.98%, 55.32%, 52.30%, and 55.26% for TAC, TEC, gas emission, and exergy destruction compared to conventional HADPSD.
Simulated annealing(sa) algorithm is a heuristic algorithm,proposed one approximation algorithm of solving optimization combinatorial problems inspired by objects in the annealing process of heating crunch. The algori...
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Simulated annealing(sa) algorithm is a heuristic algorithm,proposed one approximation algorithm of solving optimization combinatorial problems inspired by objects in the annealing process of heating crunch. The algorithm is superior to the traditional greedy algorithm,which avoids falling into local optimum and reaches global optimum. There are often some problems to find the shortest path,etc in the logistics and distribution network, and we need optimization for logistics and distribution path in order to achieve the shortest,best,most economical,and so on. The paper uses an example of sa algorithm validation to verify it,and the method is proved to be feasible.
A Based-Similarity Combination SPL Test Suite Generation Method can be used to compute coverage,replace poor test cases and generate high coverage test *** the way to compute mutation and make test suite generation ad...
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A Based-Similarity Combination SPL Test Suite Generation Method can be used to compute coverage,replace poor test cases and generate high coverage test *** the way to compute mutation and make test suite generation adapt to the current coverage from great mutation,the optimization procedure can be more ***,adaptive simulated annealing algorithm combined with simplified GA algorithm,which can ensure local optimization accuracy and take the whole situation into global searching space accordingly,is a good choice for combinatorial optimization *** with 6 feature model from SPLOT shows that the new test suite generation method can achieve smaller-size test suite with higher coverage.
A Based-Similarity Combination SPL Test Suite Generation Method can be used to compute coverage, replace poor test cases and generate high coverage test suite. Improve the way to compute mutation and make test suite g...
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A Based-Similarity Combination SPL Test Suite Generation Method can be used to compute coverage, replace poor test cases and generate high coverage test suite. Improve the way to compute mutation and make test suite generation adapt to the current coverage from great mutation, the optimization procedure can be more efficient. Meanwhile, adaptive simulated annealing algorithm combined with simplified GA algorithm, which can ensure local optimization accuracy and take the whole situation into global searching space accordingly, is a good choice for combinatorial optimization problems. Experiments with 6 feature model from SPLOT shows that the new test suite generation method can achieve smaller-size test suite with higher coverage.
The objective of this paper is to illustrate a comparative study on the performance of DWT with the multiplier reducing algorithms. The optimization techniques were based on the identification of algorithms, which cou...
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ISBN:
(纸本)9781467350891
The objective of this paper is to illustrate a comparative study on the performance of DWT with the multiplier reducing algorithms. The optimization techniques were based on the identification of algorithms, which could exploit the FPGA features. Discrete Wavelet Transform (DWT) is one of the most used techniques for image compression and is applied in a large category of applications for multi resolution analysis of signals. DWT can provide significant compression ratios without great loss of visual quality than the previous techniques such as the Discrete Cosine Transform (DCT) and the Discrete Fourier Transform (DFT). This work provides an analysis between the conventional VLSI implementation techniques as against an area efficient realization approach. This is expected to provide a reduction in hardware complexity and also an increase in computational speed. The reduction in the resource utilization improves the system performance by means of reduction in power consumption as well as the reduction in delay.
This paper proposed a new method for unmanned aerial vehicle（UAV） path planning based on K-means algorithm and simulated annealing（sa） algorithm, which solves the problem for multi-UAVs with multi-mission under co...
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This paper proposed a new method for unmanned aerial vehicle（UAV） path planning based on K-means algorithm and simulated annealing（sa） algorithm, which solves the problem for multi-UAVs with multi-mission under complicated constraints. Firstly, the model is established for the no-fly zone, the target zone and the valid zone for cruise within it in the mission area. Then, the decomposition technique decomposes the valid area into multiple sub-target points reasonably. Secondly, the K-means algorithm is used to cluster the target points of UAV cruise, which solves the problem for UAV cruise range and scheduling issues. Combining the sa algorithm for the similar sub-target route planning, this technique increases the coverage of the UAVs in the sub-target area of cruise valid area. Finally, taking the real data of UAVs in earthquake relief as an example, the effectiveness and robustness of the proposed method is validated by simulation experiments.
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