摘要 :
In this work, we have modified the optimization algorithm in [1] by incorporating the local information, which is progressively obtained during the iterations of the algorithm.Whenever two suitable descent directions are detected,...
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In this work, we have modified the optimization algorithm in [1] by incorporating the local information, which is progressively obtained during the iterations of the algorithm.Whenever two suitable descent directions are detected, a new point is produced through a linesearch along the composite direction. The results of the numerical examples have demonstrated the improved efficiency.
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摘要 :
In this work, we have modified the optimization algorithm in [1] by incorporating the local information, which is progressively obtained during the iterations of the algorithm.Whenever two suitable descent directions are detected,...
展开
In this work, we have modified the optimization algorithm in [1] by incorporating the local information, which is progressively obtained during the iterations of the algorithm.Whenever two suitable descent directions are detected, a new point is produced through a linesearch along the composite direction. The results of the numerical examples have demonstrated the improved efficiency.
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摘要 :
In this work, we have modified the optimization algorithm in [1] by incorporating the local information, which is progressively obtained during the iterations of the algorithm. Whenever two suitable descent directions are detected...
展开
In this work, we have modified the optimization algorithm in [1] by incorporating the local information, which is progressively obtained during the iterations of the algorithm. Whenever two suitable descent directions are detected, a new point is produced through a linesearch along the composite direction. The results of the numerical examples have demonstrated the improved efficiency.
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In defence sector, the combined effort of both commander and captain helps to prepare an efficient troop with optimized strength, before each combat operation. A continuous attempt is being maintained in order to maximize both pro...
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In defence sector, the combined effort of both commander and captain helps to prepare an efficient troop with optimized strength, before each combat operation. A continuous attempt is being maintained in order to maximize both protection of the combatants in the troop and rate of killing enemies. Hence during the process of selecting the best troop, there could be a number of readjustments, shuffling and exchanging mechanisms applied over the combatants. These mechanisms or the course of actions taken by a commander/captain has been modelled to an optimization algorithm, proposed as 'Troop Search Optimization (TSO) algorithm' in this paper. TSO takes utmost care to balance both exploration and exploitation in the population during simulation. In order to realize the better strength of TSO together with proposed crossover, a set of 11 unconstrained benchmark problems have been solved by TSO and the results are compared. The numerical results and statistical analysis confirm the better strength of TSO over many of the recently established bio-inspired algorithms including ABC, GABC, TLBO, ITLBO, HS and IBA.
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Linear-space cost-bounded iterative deepening search algorithm (IDA~*) can solve discrete optimization problems (DOPs) that are intractable to best-first branch-and-bound (BFBB). But IDA~* may potentially incur huge overhead due t...
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Linear-space cost-bounded iterative deepening search algorithm (IDA~*) can solve discrete optimization problems (DOPs) that are intractable to best-first branch-and-bound (BFBB). But IDA~* may potentially incur huge overhead due to repetitive expansion of nodes and hence may consume inordinate amount of time even on a reasonable sized DOR MREC, an available-memory version of IDA~*, tries to mitigate IDA~* 's overhead by storing as many nodes as it can but it still incurs substantial overhead due to re-expansion of essential nodes (nodes with cost < optimal solution cost (C~*)) and expansion of nodes at the boundary of essential search space (nodes with cost = C~*). In this paper, we present two novel enhancements to MREC to address the two overheads identified above. Empirical results obtained on 100 search trees show an average reduction of up to 100% and 25% in overheads due to boundary node expansion and essential node re-expansion respectively, relative to MREC.
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Linear-space cost-bounded iterative deepening search algorithm (IDA{sup}*) can solve discrete optimization problems (DOPs) that are intractable to best-first branch-and-bound (BFBB). But IDA{sup}* may potentially incur huge overhe...
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Linear-space cost-bounded iterative deepening search algorithm (IDA{sup}*) can solve discrete optimization problems (DOPs) that are intractable to best-first branch-and-bound (BFBB). But IDA{sup}* may potentially incur huge overhead due to repetitive expansion of nodes and hence may consume inordinate amount of time even on a reasonable sized DOP. MREC, an available-memory version of IDA{sup}*, tries to mitigate IDA{sup}* 's overhead by storing as many nodes as it can but it still incurs substantial overhead due to re-expansion of essential nodes (nodes with cost < optimal solution cost (C{sup}*)) and expansion of nodes at the boundary of essential search space (nodes with cost = C{sup}*). In this paper, we present two novel enhancements to MREC to address the two overheads identified above. Empirical results obtained on 100 search trees show an average reduction of up to 100% and 25% in overheads due to boundary node expansion and essential node re-expansion respectively, relative to MREC.
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In this study, a novel metaheuristic called the multiple trajectory search (MTS) is proposed to solve the uncapacitated facility location problem (UFLP). The multiple trajectory search hybridizes a global search method (the Trajec...
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In this study, a novel metaheuristic called the multiple trajectory search (MTS) is proposed to solve the uncapacitated facility location problem (UFLP). The multiple trajectory search hybridizes a global search method (the Trajectory_Search) and a local search method (the Variable_Neighborhood_Search). The application of the multiple trajectory search to the benchmarks ORLIB and GHOSH had been conducted. The performance comparison with other state-of-the-art methods reveals that the proposed method is very competitive.
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摘要 :
In this study, a novel metaheuristic called the multiple trajectory search (MTS) is proposed to solve the uncapacitated facility location problem (UFLP). The multiple trajectory search hybridizes a global search method (the Trajec...
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In this study, a novel metaheuristic called the multiple trajectory search (MTS) is proposed to solve the uncapacitated facility location problem (UFLP). The multiple trajectory search hybridizes a global search method (the Trajectory_Search) and a local search method (the Variable_Neighborhood_Search). The application of the multiple trajectory search to the benchmarks ORLIB and GHOSH had been conducted. The performance comparison with other state-of-the-art methods reveals that the proposed method is very competitive.
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Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and featur...
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Through the influx of information content on the Internet, a number of image search methodologies have been presented and implemented to increase the accuracy of image retrieval including keywords, object classification and feature processing. Both keyword and object classification models rely heavily on human subjects, which is time-consuming and error-prone with inconsistency in word agreement. We propose two feature processing methods without human intervention. The feature collage algorithm compares images based on particular features such as color histogram whereas the feature independent algorithm considers each feature's dimension as independent contributors to the image quality. Using query-by-example, we organize images using rank aggregation methods, previously applied in text information retrieval. We show through empirical experimentation the benefits of our feature processing algorithms over traditional CBIR approaches.
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Many proteins carry out their biological functions by forming the characteristic tertiary structures.Therefore, the search of the stable states of proteins by molecular simulations is important to understand their functions and st...
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Many proteins carry out their biological functions by forming the characteristic tertiary structures.Therefore, the search of the stable states of proteins by molecular simulations is important to understand their functions and stabilities.However, getting the stable state by conformational search is difficult, because the energy landscape of the system is characterized by many local minima separated by high energy barriers.In order to overcome this difficulty, various sampling and optimization methods for conformations of proteins have been proposed.In this study, we propose a new conformational search method for proteins by using genetic crossover and Metropolis criterion.We applied this method to an α-helical protein.The conformations obtained from the simulations are in good agreement with the experimental results.
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