A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PG...
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A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PGA is a feasible and effective optimization tool for inverse heat conduction problems.
This paper presents a parallelgenetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into sub-populations, and in e...
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This paper presents a parallelgenetic simulated annealing (PGSA) algorithm that has been developed and applied to optimize continuous problems. In PGSA, the entire population is divided into sub-populations, and in each sub-population the algorithm uses the local search ability of simulated annealing after crossover and mutation. The best individuals of each subpopulation are migrated to neighboring ones after a certain number of epochs. An implementation of the algorithm is discussed and the performance is evaluated against a standard set of test functions. PGSA shows some remarkable improvement in comparison with the conventional parallel genetic algorithm and the breeder geneticalgorithm (BGA). (c) 2005 Elsevier B.V. All rights reserved.
Image encryption is an efficient technique to protect image content from unauthorized parties. In this paper a parallel image encryption method based on bitplane decomposition is proposed. The original grayscale image...
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Image encryption is an efficient technique to protect image content from unauthorized parties. In this paper a parallel image encryption method based on bitplane decomposition is proposed. The original grayscale image is converted to a set of binary images by local binary pattern (LBP) technique and bitplane decomposition (BPD) methods. Then, permutation and substitution steps are performed by geneticalgorithm (GA) using crossover and mutation operations. Finally, these scrambled bitplanes are combined together to obtain encrypted image. Instead of random population selection in GA, a deterministic method with security keys is utilized to improve security level. The proposed encryption method has parallel processing capability for multiple bitplanes encryption. This distributed GA with multiple populations increases encryption speed and makes it suitable for real-time applications. Simulations and security analysis are done to demonstrate efficiency of our algorithm.
A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PG...
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A parallel genetic algorithm ( PGA) is proposed for the solution of two-dimensional inverse heat conduction problems involving unknown thermophysical material properties. Experimental results show that the proposed PGA is a feasible and effective optimization tool for inverse heat conduction problems.
geneticalgorithms (GA) are widely used in the literature to extract interesting association rules. However, they are time consuming mainly due to the growing size of databases. To speed up this process, we propose tw...
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geneticalgorithms (GA) are widely used in the literature to extract interesting association rules. However, they are time consuming mainly due to the growing size of databases. To speed up this process, we propose two parallel GAs (ARMGPU and ARM-CPU/GPU). In ARM-GPU, parallelism is used to compute the fitness which is the most time consuming task;while, ARM-CPU/GPU proposes a two-level-based parallel GA. In the first level, the different cores of the CPU execute a GAARM on a sub-population. The second level of parallelism is used to compute the fitness, in parallel, on GPU. To validate the proposed two parallel GAs, several tests were conducted to solve well-known large ARM instances. Obtained results show that our parallelalgorithms outperform state-of-the-art exact algorithms (APRIORI and FP-GROWTH) and approximate algorithms (SEGPU and ME-GPU) in terms of execution time.
This paper evaluates a parallel genetic algorithm (GA) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is investigated on two types of arrangements of heterogeneous computi...
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ISBN:
(纸本)1595930108
This paper evaluates a parallel genetic algorithm (GA) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is investigated on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangement of computing capability. Their differences in chromosome variety, migration frequency and solution quality are investigated. The results in this paper can help to design parallel GAs in grid computing environments.
As Third Generation (3G) mobile networks start to be implemented, there is a need for effective network planning. However, deciding upon the optimum placement for the base stations of the networks is a complex task re...
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ISBN:
(纸本)0769520804
As Third Generation (3G) mobile networks start to be implemented, there is a need for effective network planning. However, deciding upon the optimum placement for the base stations of the networks is a complex task requiring vast computational resource. This paper discusses the conflicting objectives of base station planning and characterises a multi-objective optimisation problem. We present a genetic encoding of the third generation mobile network planning problem and parallel genetic algorithms to solve it.
Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is appl...
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Dynamic Time Warping (DTW) is a common technique widely used for nonlinear time normalization of different utterances in many speech recognition systems. Two major problems are usually encountered when the DTW is applied for recognizing speech utterances: (i) the normalization factors used in a warping path;and (ii) finding the K-best warping paths. Although DTW is modified to compute multiple warping paths by using the Tree-Trellis Search (TTS) algorithm, the use of actual normalization factor still remains a major problem for the DTW. In this paper, a parallelgenetic Time Warping (PGTW) is proposed to solve the above said problems. A database extracted from the TIMIT speech database of 95 isolated words is set up for evaluating the performance of the PGTW. In the database, each of the first 15 words had 70 different utterances, and the remaining 80 words had only one utterance. For each of the 15 words, one utterance is arbitrarily selected as the test template for recognition. Distance measure for each test template to the utterances of the same word and to those of the 80 words is calculated with three different time warping algorithms: TTS, PGTW and Sequential genetic Time Warping (SGTW). A Normal Distribution Model based on Rabiner(23) is used to evaluate the performance of the three algorithms analytically. The analyzed results showed that the PGTW had performed better than the TTS. It also showed that the PGTW had very similar results as the SGTW, but about 30% CPU time is saved in the single processor system.
A new multi-objective evolutionary algorithm, called selective migration parallel genetic algorithm (SMPGA) was presented in this paper, which designs a new migration strategy and qualification based on the adaptive g...
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ISBN:
(纸本)9781424451821
A new multi-objective evolutionary algorithm, called selective migration parallel genetic algorithm (SMPGA) was presented in this paper, which designs a new migration strategy and qualification based on the adaptive grid. In SMPGA, a searching population and a elite population evolve at the same time;unique migration strategy and qualification are used to keep and improve the convergence and diversity of the Pareto optimal set. Besides, according to their different purposes, the two populations adopt different crossover strength. Simulation results show that SMPGA can find accurate and uniform Pareto optimal solutions on different multi-objective problems.
Recently, heat-integrated pressure-swing distillation (HIPSD) has been explored for recovering isopropanol and benzene from wastewater, but the process remains highly energy-intensive. Given the dilute nature of the f...
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Recently, heat-integrated pressure-swing distillation (HIPSD) has been explored for recovering isopropanol and benzene from wastewater, but the process remains highly energy-intensive. Given the dilute nature of the feed, intensified extractive distillation with an integrated preconcentration column (IED) is more suitable. However, previous researches have often overlooked the critical role of pressure and lacked rigorous optimization of operating pressure in such systems. In this article, we developed novel extractive pressure-swing distillation with integrated feed preconcentration/solvent recovery column (IEPSD) by introducing a pressure-swing configuration and incorporate energy-saving technologies to achieve more sustainable and cost-effective separation processes. First, thermodynamic analysis is conducted to explore the effect of pressure on extractive pressure-swing distillation. Then, a parallel genetic algorithm is applied for rigorous optimization, followed by the application of heat integration and heat pump. The IEPSD is superior to IED in terms of total annual cost (TAC) and CO2 emission. With the inclusion of energy-saving technologies, IEDHI and IEPSDHI further reduced TAC by 5.29% and 7.40%, and cut CO2 emissions by 25.18% and 24.03%, respectively, compared to their base processes. The use of a heat pump is particularly advantageous for IEPSD due to its lower boiling temperatures under vacuum pressure, leading to the development of IEPSDHIHP, which offered an additional 22.36% CO2 reduction and marginally lower TAC than IEPSDHI. Ultimately, IEPSDHIHP proves to be the best option, offering a 51.15% TAC reduction and 68.42% CO2 emissions reduction compared to HIPSD. In summary, the developed IED and IEPSD processes, especially with heat integration and heat pump technologies, offer significant economic and environmental advantages over conventional HIPSD.
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