Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usua...Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper first proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. Second, the strategy is combined with the setting of other parameters to form a new ACO method. Then, the proposed algorithm can be proved to converge to the global optimal solution. Finally, the experimental results of computing traveling salesman problems and film-copy deliverer problems also indicate that the proposed ACO approach is more effective than other ant methods and non-ant methods.展开更多
With the explosive growth of surveillance video data,browsing videos quickly and effectively has become an urgent problem.Video key frame extraction has received widespread attention as an effective solution.However,a...With the explosive growth of surveillance video data,browsing videos quickly and effectively has become an urgent problem.Video key frame extraction has received widespread attention as an effective solution.However,accurately capturing the local motion state changes of moving objects in the video is still challenging in key frame extraction.The target center offset can reflect the change of its motion state.This observation proposed a novel key frame extraction method based on moving objects center offset in this paper.The proposed method utilizes the center offset to obtain the global and local motion state information of moving objects,and meanwhile,selects the video frame where the center offset curve changes suddenly as the key frame.Such processing effectively overcomes the inaccuracy of traditional key frame extraction methods.Initially,extracting the center point of each frame.Subsequently,calculating the center point offset of each frame and forming the center offset curve by connecting the center offset of each frame.Finally,extracting candidate key frames and optimizing them to generate final key frames.The experimental results demonstrate that the proposed method outperforms contrast methods to capturing the local motion state changes of moving objects.展开更多
Image matting is an essential image processing technology due to its wide range of applications.Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecti...Image matting is an essential image processing technology due to its wide range of applications.Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs.It is essentially a large-scale multi-peak optimization problem of pixel pairs.Previous study shows that particle swarm optimization(PSO)can effectively optimize the pixel pairs.However,it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima.To address this problem,this work presents a parameter-free strategy for PSO called adaptive convergence speed controller(ACSC).ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator(CPPRO)and pixel pair reset operator(PPRO)during the iteration.ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution.PPRO is performed to avoid premature convergence when the alpha mattes regarding two selecleu particles ae liglly siimilau.Expeiilfental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.展开更多
The problem of robust alignment of batches of images can be formulated as a low-rank matrix optimization problem, relying on the similarity of well-aligned images. Going further, observing that the images to be aligne...The problem of robust alignment of batches of images can be formulated as a low-rank matrix optimization problem, relying on the similarity of well-aligned images. Going further, observing that the images to be aligned are sampled from a union of low-rank subspaces, we propose a new method based on subspace recovery techniques to provide more robust and accurate alignment. The proposed method seeks a set of domain transformations which are applied to the unaligned images so that the resulting images are made as similar as possible. The resulting optimization problem can be linearized as a series of convex optimization problems which can be solved by alternative sparsity pursuit techniques. Compared to existing methods like robust alignment by sparse and low-rank models, the proposed method can more effectively solve the batch image alignment problem,and extract more similar structures from the misaligned images.展开更多
Purpose–The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem.In mobile ad hoc network(MANET),multicast routing is a non-dete...Purpose–The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem.In mobile ad hoc network(MANET),multicast routing is a non-deterministic polynomial-complete problem that deals with the various objectives and constraints.Quality of service(QoS)in the multicast routing problem mainly depends on cost,delay,jitter and bandwidth.So the cost,delay,jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols.However,mobile node battery energy is finite and the network lifetime depends on node battery energy.Ifthe batterypowerconsumptionishigh inany one ofthe nodes,the chances ofnetwork’s lifereduction due to path breaks are also more.On the other hand,node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time.Hence,the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem.Design/methodology/approach–Recently,many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem,although it obviously is a multi-objective optimization problem.In the MOMR-DE,the network lifetime,cost,delay,jitter and bandwidth are considered as five objectives.Furthermore,three QoS constraints which are maximum allowed delay,maximum allowed jitter and minimum requested bandwidth are included.In addition,we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth,minimize cost,delay and jitter.Findings–Two sets of experiments are conducted and compared with other algorithms for these problems.Thesimulation results showthat our proposedmethod is capableof achieving faster convergence andis more preferable for multicast routing in MANET.Originality/value–In MANET,most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem.However,this paper proposes a multi-objective differential evolution algorithm to resolvemulticast routingproblem,andthe proposed algorithmis capableof achieving faster convergence and more preferable for multicast routing.展开更多
文摘Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper first proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. Second, the strategy is combined with the setting of other parameters to form a new ACO method. Then, the proposed algorithm can be proved to converge to the global optimal solution. Finally, the experimental results of computing traveling salesman problems and film-copy deliverer problems also indicate that the proposed ACO approach is more effective than other ant methods and non-ant methods.
基金This work was supported by the National Nature Science Foundation of China(Grant No.61702347,61772225)Natural Science Foundation of Hebei Province(Grant No.F2017210161).
文摘With the explosive growth of surveillance video data,browsing videos quickly and effectively has become an urgent problem.Video key frame extraction has received widespread attention as an effective solution.However,accurately capturing the local motion state changes of moving objects in the video is still challenging in key frame extraction.The target center offset can reflect the change of its motion state.This observation proposed a novel key frame extraction method based on moving objects center offset in this paper.The proposed method utilizes the center offset to obtain the global and local motion state information of moving objects,and meanwhile,selects the video frame where the center offset curve changes suddenly as the key frame.Such processing effectively overcomes the inaccuracy of traditional key frame extraction methods.Initially,extracting the center point of each frame.Subsequently,calculating the center point offset of each frame and forming the center offset curve by connecting the center offset of each frame.Finally,extracting candidate key frames and optimizing them to generate final key frames.The experimental results demonstrate that the proposed method outperforms contrast methods to capturing the local motion state changes of moving objects.
基金supported by the National Nat-ural Science Foundation of China(Grant Nos.61772225,61876207,61502088)National Key R&D Program of China(2018YFCO823803,2018YFCO823802)+7 种基金Zhongshan Science and Technology Research Project of Social welfare(2019B2010)Guangdong Natural Science Fundsfor Distinguished Young Scholar(2014A030306050)Guangdong High-level personnel of special support program(2014TQ01X664)International Cooperator Project of Guangzhou(201807010047)National Natural Scicnce Foundation of Guangdong(2018B030311046)Guangdong University Key Platforms and Research Projects(2018KZDXMO66,2017KZDXM081,2015KQNCX153)Guangzhou Science and Technology Projects(201802010007,201804010276)Youth science and technologytalents cultivating object of Guizhou province(Qian education cooperation KY word[2016]165).
文摘Image matting is an essential image processing technology due to its wide range of applications.Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs.It is essentially a large-scale multi-peak optimization problem of pixel pairs.Previous study shows that particle swarm optimization(PSO)can effectively optimize the pixel pairs.However,it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima.To address this problem,this work presents a parameter-free strategy for PSO called adaptive convergence speed controller(ACSC).ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator(CPPRO)and pixel pair reset operator(PPRO)during the iteration.ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution.PPRO is performed to avoid premature convergence when the alpha mattes regarding two selecleu particles ae liglly siimilau.Expeiilfental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.
基金supported by the National Natural Science Foundation of China (Grant Nos. 61573150, 61573152, 61370185, 61403085, and 51275094)Guangzhou Project Nos. 201604016113 and 201604046018
文摘The problem of robust alignment of batches of images can be formulated as a low-rank matrix optimization problem, relying on the similarity of well-aligned images. Going further, observing that the images to be aligned are sampled from a union of low-rank subspaces, we propose a new method based on subspace recovery techniques to provide more robust and accurate alignment. The proposed method seeks a set of domain transformations which are applied to the unaligned images so that the resulting images are made as similar as possible. The resulting optimization problem can be linearized as a series of convex optimization problems which can be solved by alternative sparsity pursuit techniques. Compared to existing methods like robust alignment by sparse and low-rank models, the proposed method can more effectively solve the batch image alignment problem,and extract more similar structures from the misaligned images.
基金supported by the National Nature Science Foundation of China(No.61370185)Nature Science Foundation of Guangdong Province(No.2016A030313135)+3 种基金Guangdong Higher School Scientific Innovation Project(No.2013KJCX0174,2013KJCX0178,2014KTSCX188)the outstanding young teacher training program of the Education Department of Guangdong Province(YQ2015158)Guangdong Provincial Science and Technology Plan Projects(No.2016A010101034,2016A010101035)Guangdong Provincial High School of International and Hong Kong,Macao and Taiwan cooperation and innovation platform and major international cooperation projects(No.2015KGJHZ027).
文摘Purpose–The purpose of this paper is to propose a multi-objective differential evolution algorithm named as MOMR-DE to resolve multicast routing problem.In mobile ad hoc network(MANET),multicast routing is a non-deterministic polynomial-complete problem that deals with the various objectives and constraints.Quality of service(QoS)in the multicast routing problem mainly depends on cost,delay,jitter and bandwidth.So the cost,delay,jitter and bandwidth are always considered as multi-objective for designing multicast routing protocols.However,mobile node battery energy is finite and the network lifetime depends on node battery energy.Ifthe batterypowerconsumptionishigh inany one ofthe nodes,the chances ofnetwork’s lifereduction due to path breaks are also more.On the other hand,node’s battery energy had to be consumed to guarantee high-level QoS in multicast routing to transmit correct data anywhere and at any time.Hence,the network lifetime should be considered as one objective of the multi-objective in the multicast routing problem.Design/methodology/approach–Recently,many metaheuristic algorithms formulate the multicast routing problem as a single-objective problem,although it obviously is a multi-objective optimization problem.In the MOMR-DE,the network lifetime,cost,delay,jitter and bandwidth are considered as five objectives.Furthermore,three QoS constraints which are maximum allowed delay,maximum allowed jitter and minimum requested bandwidth are included.In addition,we modify the crossover and mutation operators to build the shortest-path multicast tree to maximize network lifetime and bandwidth,minimize cost,delay and jitter.Findings–Two sets of experiments are conducted and compared with other algorithms for these problems.Thesimulation results showthat our proposedmethod is capableof achieving faster convergence andis more preferable for multicast routing in MANET.Originality/value–In MANET,most metaheuristic algorithms formulate the multicast routing problem as a single-objective problem.However,this paper proposes a multi-objective differential evolution algorithm to resolvemulticast routingproblem,andthe proposed algorithmis capableof achieving faster convergence and more preferable for multicast routing.
