1,721,453 research outputs found
Differences in the external morphology of two sympatric species of bottlenose dolphins (Genus tursiops) in the waters of China.
No full textOsteological differences between two sympatric forms of bottlenose dolphins (genus Tursiops) in Chinese waters.
No full textMitochondrial DNA analysis of sympatric morphotypes of bottlenose dolphins (genus: Tursiops) in Chinese waters.
No full textAnt colony optimization for the nonlinear resource allocation problem
No full text[[abstract]]The nonlinear resource allocation problem addresses the important issue which seeks to find an optimal allocation of a limited amount of resource to a number of tasks for optimizing a nonlinear objective over the given resource constraint. Relevant literature has been focused on the use of mathematical programming approaches, few researches based on meta-heuristic algorithms have been conducted. In this paper we present an ant colony optimization algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we incorporate adaptive resource bounds to guide the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. Also, our method converges at a fast rate and a reliable performance guarantee is provided through a worst-case analysis. (c) 2005 Elsevier Inc. All rights reserved.[[note]]SC
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Serial analysis of fat-containing macrophages in bronchoalveolar lavage fluid in a patient with fat embolism syndrome
No full textOptimal multiple-objective resource allocation using hybrid particle swarm optimization and adaptive resource bounds technique
No full text[[abstract]]The multiple-objective resource allocation problem (MORAP) seeks for an allocation of resource to a number of activities such that a set of objectives are optimized simultaneously and the resource constraints are satisfied. MORAP has many applications, such as resource distribution, project budgeting, software testing, health care resource allocation, etc. This paper addresses the nonlinear MORAP with integer decision variable constraint. To guarantee that all the resource constraints are satisfied, we devise an adaptive-resource-bound technique to construct feasible solutions. The proposed method employs the particle swarm optimization (PSO) paradigm and presents a hybrid execution plan which embeds a hill-climbing heuristic into the PSO for expediting the convergence. To cope with the optimization problem with multiple objectives, we evaluate the candidate solutions based on dominance relationship and a score function. Experimental results manifest that the hybrid PSO derives solution sets which are very close to the exact Pareto sets. The proposed method also outperforms several representatives of the state-of-the-art algorithms on a simulation data set of the MORAP. (C) 2007 Elsevier B.V. All rights reserved.[[note]]SC
A particle swarm optimization approach to the nonlinear resource allocation problem
No full text[[abstract]]The resource allocation problem seeks to find an optimal allocation of a limited amount of resource to a number of activities for optimizing the objective under the resource constraint. Most existing methods use mathematical programming techniques, but they may fail to derive exact solutions for large-sized problems with reasonable time. An alternative is to use meta-heuristic algorithms for obtaining approximate solutions. This paper presents a particle swarm optimization (PSO) algorithm for conquering the nonlinear resource allocation problem. To ensure the resource constraint is satisfied, we propose adaptive resource bounds for guiding the search. The experimental results manifest that the proposed method is more effective and efficient than a genetic algorithm. The convergence behavior of the proposed method is analyzed by observing the variations of particle entropy. Finally, a worst-case analysis is conducted to provide a reliable performance guarantee. (c) 2006 Elsevier Inc. All rights reserved.[[note]]SC
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