In this study we are concerned with the general problem of choosing from a set of endangered species T a subset S of k species to protect as a priority. Here, the interest to protect the species of S is assessed by th...
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In this study we are concerned with the general problem of choosing from a set of endangered species T a subset S of k species to protect as a priority. Here, the interest to protect the species of S is assessed by the resulting expected phylogenetic diversity (ePD) of the set T, a widely used criterion for measuring the expected amount of evolutionary history associated with T. We consider that the survival of the protected species is assured and, on the contrary, that there is a risk of extinction for the unprotected species. The problem is easy to solve by a greedy type method if the extinction probabilities of the unprotected species are known but these probabilities are generally not easy to quantify. We show in this note that the choice of the precise values attributed to the extinction probabilities-provided it respects the rank of imperilment of each species-is not as decisive as might be feared for the considered problem. The values of these probabilities have a clear impact on the selection of the species to be protected but a little impact on the resulting ePD. More precisely, if T (1) and T (2) are the two optimal subsets of species corresponding to two scenarios (two different sets of probabilities) the ePDs of T (1) and T (2), calculated with the probabilities of the first scenario-or with the probabilities of the second scenario-are not very different.
The trend of traveling has increased in recent years along with the easy access to information. Travelers create the itinerary before they are travelling to an area. However, creating itinerary is not easy, many facto...
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The trend of traveling has increased in recent years along with the easy access to information. Travelers create the itinerary before they are travelling to an area. However, creating itinerary is not easy, many factors need to be considered before deciding where to go, such as the “must visited” places in that area, distance between tourist attractions to be visited within a day, preferred tourist attractions, and other information about the places. Generating the optimized distance itinerary is similar as a Traveling Salesman Problem (TSP). The purpose of this research is to study the combination of simulated annealing and greedy algorithms to create an itinerary. The initialization process of the simulated annealing algorithm uses greedy algorithm searching technique and then proceeded with existing optimization step in simulated annealing method. This paper proposed a method of enhanced simulated annealing algorithm and the experiment results show the enhanced simulated annealing increase the effectiveness of simulated annealing algorithm which produce the shortest distance needed to travel places around an area.
Optimizing deployment of charging base stations in wireless rechargeable sensor networks can considerably reduce the cost. Previously, the charging base stations are simply installed at some fixed special points (e.g....
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Optimizing deployment of charging base stations in wireless rechargeable sensor networks can considerably reduce the cost. Previously, the charging base stations are simply installed at some fixed special points (e.g., the grid points) after partitioning the area distributed by sensor nodes. In this paper, a new algorithm of planning the charging base stations is proposed based on the greedy algorithm and the location relationship of the sensor nodes. The proposed algorithm exploits the local search ability and avoids falling into an exponential increase of the number of the charging base stations (i.e., combinatorial explosion). The simulated results show that the proposed algorithm can result in less number and flexible deployed locations of the charging base stations. In addition, this algorithm provides a novel solution for the point coverage problems.
We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the dr...
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We develop a nature-inspired stochastic population-based algorithm and call it discrete particle swarm optimization to find extended two-stage adaptive optimal designs that allow three target response rates for the drug in a phase II trial. Our proposed designs include the celebrated Simon's two-stage design and its extension that allows two target response rates to be specified for the drug. We show that discrete particle swarm optimization not only frequently outperforms greedy algorithms, which are currently used to find such designs when there are only a few parameters;it is also capable of solving design problems posed here with more parameters that greedy algorithms cannot solve. In stage 1 of our proposed designs, futility is quickly assessed and if there are sufficient responders to move to stage 2, one tests one of the three target response rates of the drug, subject to various user-specified testing error rates. Our designs are therefore more flexible and interestingly, do not necessarily require larger expected sample size requirements than two-stage adaptive designs. Using a real adaptive trial for melanoma patients, we show our proposed design requires one half fewer subjects than the implemented design in the study.
The higher-order orthogonal iteration (HOOI) has been popularly used for finding a best low-multilinear rank approximation of a tensor. However, its convergence is still an open question. In this paper, we first analy...
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The higher-order orthogonal iteration (HOOI) has been popularly used for finding a best low-multilinear rank approximation of a tensor. However, its convergence is still an open question. In this paper, we first analyse a greedy HOOI, which updates each factor matrix by selecting from the best candidates one that is closest to the current iterate. Assuming the existence of a block-nondegenerate cluster point, we establish its global iterate sequence convergence through the so-called Kurdyka-?ojasiewicz property. In addition, we show that if the starting point is sufficiently close to any block-nondegenerate globally optimal solution, the greedy HOOI produces an iterate sequence convergent to a globally optimal solution. Relating the iterate sequence by the original HOOI to that by the greedy HOOI, we then show that the original HOOI has global convergence on the multilinear subspace sequence and thus positively address the open question.
This study proposed a novel path-planning method for multilink manipulators along the shortest path against obstacles in the plane including three steps. First, the shortest path from the source to the destination is ...
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This study proposed a novel path-planning method for multilink manipulators along the shortest path against obstacles in the plane including three steps. First, the shortest path from the source to the destination is calculated without considering obstacles. Second, whether the optimal path crosses obstacles or not. Finally, a new turning point with shortest path to the collided point and let a new path across this collided point. The shortest path is calculated repeatly until that path does not encounter any obstacle. The proposed greedy algorithm reduces time complexity and information size, and shows good effectiveness for the path planning problem.
Several problems in operations research, such as the assembly line crew scheduling problem and the k-partitioning problem can be cast as the problem of finding the intra-column rearrangement (permutation) of a matrix ...
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Several problems in operations research, such as the assembly line crew scheduling problem and the k-partitioning problem can be cast as the problem of finding the intra-column rearrangement (permutation) of a matrix such that the row sums show minimum variability. A necessary condition for optimality of the rearranged matrix is that for every block containing one or more columns it must hold that its row sums are oppositely ordered to the row sums of the remaining columns. We propose the block rearrangement algorithm with variance equalization (BRAVE) as a suitable method to achieve this situation. It uses a carefully motivated heuristic-based on an idea of variance equalization-to find optimal blocks of columns and rearranges them. When applied to the number partitioning problem, we show that BRAVE outperforms the well-known greedy algorithm and the Karmarkar-Karp differencing algorithm.
In this paper we consider the generalized Walsh system and a problem L-1-convergence of greedy algorithm of functions after changing the values on small set.
In this paper we consider the generalized Walsh system and a problem L-1-convergence of greedy algorithm of functions after changing the values on small set.
Car-sharing system with electric cars is a very convenient service for urban transportation: it allows users to pick up a vehicle at a station and rent it during a short time. To manage this kind of system in the best...
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Car-sharing system with electric cars is a very convenient service for urban transportation: it allows users to pick up a vehicle at a station and rent it during a short time. To manage this kind of system in the best way, it is necessary to solve the critical problem of vehicle stock imbalance across the stations. Several decision levels must be considered to balance the car distribution by taking into account the quality of service and the system operation cost. To this end, a linear programming model is proposed to formalize the problem in a mathematical framework, which allows the computation of optimal vehicle distribution strategies. To make our solution time efficient and usable for solving large problems, a greedy algorithm and a tabu search algorithm are proposed. These two algorithms are applied to the Auto Bleue network in Nice and its surrounding (France) using extensive simulations. Besides, an integrated mapping method is provided within the Geographical Information System QGIS to estimate flows and their locations. Numerical results demonstrate that the tabu search algorithm is able to find near-optimal solutions and good compromises between client satisfaction, number of staff agents and vehicles used, and computing time.
Regression testing is an expensive procedure that is implemented during maintenance phase of the Software Development Life Cycle of evolving software. During this process, test case prioritization is one of the strate...
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Regression testing is an expensive procedure that is implemented during maintenance phase of the Software Development Life Cycle of evolving software. During this process, test case prioritization is one of the strategies followed in which test cases are organized in a fashion so as to enhance efficiency in achieving some performance goal. During the process, there could be several aspects to be kept in mind due to resources constraints such as fault severity detected per unit of test cost, severity detection per test case execution, and execution time of test cases to detect all the faults. Keeping all such constraints in mind, the test case prioritization problem becomes a multi-objective problem where some of the objectives have to be maximized and the remaining ones minimized. In this study, experiments were performed on different versions of five web applications. The problem instance was found to vary from 5 5 test cases versus fault matrix, to 125 125 matrix. Random approach, 2-opt algorithm, improved 2-opt algorithm, greedy approach, additional greedy approach, Weighted Genetic algorithm and Non-dominated Sorting Genetic algorithm-II (NSGA-II) were applied to a generate prioritized test sequence which maximizes the Cost Cognizant Average Percentage of Fault Detection value, severity detection and minimizes test case execution cost to expose all the faults. The performances of these algorithms are compared, keeping these parameters in mind, and it is concluded that the performance of NSGA-II algorithm is better than that of all the other tested algorithms throughout all the experiments.
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