The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algor...The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system （AIS） with a strong local search ability of extremal optimization （EO） algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.展开更多
When solving a mathematical problem, we sometimes encounter a situation where we can not reach a correct answer in spite of acquiring knowledge and formula necessary for the solution. The reason can be attributed to t...When solving a mathematical problem, we sometimes encounter a situation where we can not reach a correct answer in spite of acquiring knowledge and formula necessary for the solution. The reason can be attributed to the lack in metacognitive abilities. Metacognitive abilities consist of comparing the difficulty of problem with own ability, proper plan of solution process, and conscious monitoring and control of solution process. The role and importance of metacognitive ability in mathematical problem solving of permutations and combinations was explored. Participants were required to solve five practical problems related to permutations and combinations. For each problem, the solution process was divided into： （1） understanding （recognition） of mathematical problem; （2） plan of solution; （3） execution of solution. Participants were also required to rate the anticipation whether they could solve it or not, and to rate the confidence of their own answer. According to the total score of five problems, the participants were categorized into the group of the high test score and the group of the low test score. As a result, at the plan and the execution processes, statistically significant differences were detected between the high and the low score groups. As for the rating on the anticipation of result and the confidence of own answer, no significant differences were found between both groups. Moreover, the relationship between the score of plan process and the score of execution process was statistically correlated. In other words, the more proper the plan process was conducted, the more proper solution the participants reached. In such a way, the importance of metacognitive ability in the solving process, especially the plan ability, was suggested.展开更多
In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper st...In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the DHPFSPVPS.DMSSA makes four improvements based on the squirrel search algorithm.Firstly,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions.Secondly,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling problems.Additionally,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search capability.Moreover,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation.Finally,two speed control energy-efficient strategies are designed to reduce TEC.Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies.The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.展开更多
A Wireless Sensors Network (WSN) is an ad-hoc network populated by small hand-held commodity devices, running on batteries called stations or sensors. Often used in hostiles and sometimes unreachable environments, sta...A Wireless Sensors Network (WSN) is an ad-hoc network populated by small hand-held commodity devices, running on batteries called stations or sensors. Often used in hostiles and sometimes unreachable environments, stations are subject to energetic constraints which can significantly decrease the network life time. Permutation routing problem is mainly found in the literature of WSN. This problem occurs when some stations have items that belong either or not to them. The goal is to send each item to its receiver. To solve this problem, several works are presented in the literature. In this paper, we present a new permutation routing protocol for multi-hop wireless sensors network that, compared to recent work in the field is more efficient in terms of conservation of sensors’ energy, which results in a longer life time of the network. Also, contrary to some other routing protocols which assume that the memory of the sensors is infinite, we show that the memory size of the sensors is limited, which in our opinion is more realistic.展开更多
The cocktail party problem,i.e.,tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously,is one of the critical problems yet to be solved to enable the wide application of au...The cocktail party problem,i.e.,tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously,is one of the critical problems yet to be solved to enable the wide application of automatic speech recognition(ASR) systems.In this overview paper,we review the techniques proposed in the last two decades in attacking this problem.We focus our discussions on the speech separation problem given its central role in the cocktail party environment,and describe the conventional single-channel techniques such as computational auditory scene analysis(CASA),non-negative matrix factorization(NMF) and generative models,the conventional multi-channel techniques such as beamforming and multi-channel blind source separation,and the newly developed deep learning-based techniques,such as deep clustering(DPCL),the deep attractor network(DANet),and permutation invariant training(PIT).We also present techniques developed to improve ASR accuracy and speaker identification in the cocktail party environment.We argue effectively exploiting information in the microphone array,the acoustic training set,and the language itself using a more powerful model.Better optimization ob jective and techniques will be the approach to solving the cocktail party problem.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No.60574063）
文摘The permutation flowshop scheduling problem （PFSP） is one of the most well-known and well-studied production scheduling problems with strong industrial background. This paper presents a new hybrid optimization algorithm which combines the strong global search ability of artificial immune system （AIS） with a strong local search ability of extremal optimization （EO） algorithm. The proposed algorithm is applied to a set of benchmark problems with a makespan criterion. Performance of the algorithm is evaluated. Comparison results indicate that this new method is an effective and competitive approach to the PFSP.
文摘When solving a mathematical problem, we sometimes encounter a situation where we can not reach a correct answer in spite of acquiring knowledge and formula necessary for the solution. The reason can be attributed to the lack in metacognitive abilities. Metacognitive abilities consist of comparing the difficulty of problem with own ability, proper plan of solution process, and conscious monitoring and control of solution process. The role and importance of metacognitive ability in mathematical problem solving of permutations and combinations was explored. Participants were required to solve five practical problems related to permutations and combinations. For each problem, the solution process was divided into： （1） understanding （recognition） of mathematical problem; （2） plan of solution; （3） execution of solution. Participants were also required to rate the anticipation whether they could solve it or not, and to rate the confidence of their own answer. According to the total score of five problems, the participants were categorized into the group of the high test score and the group of the low test score. As a result, at the plan and the execution processes, statistically significant differences were detected between the high and the low score groups. As for the rating on the anticipation of result and the confidence of own answer, no significant differences were found between both groups. Moreover, the relationship between the score of plan process and the score of execution process was statistically correlated. In other words, the more proper the plan process was conducted, the more proper solution the participants reached. In such a way, the importance of metacognitive ability in the solving process, especially the plan ability, was suggested.
基金supported by the Key Research and Development Project of Hubei Province(Nos.2020BAB114 and 2023BAB094).
文摘In the manufacturing industry,reasonable scheduling can greatly improve production efficiency,while excessive resource consumption highlights the growing significance of energy conservation in production.This paper studies the problem of energy-efficient distributed heterogeneous permutation flowshop problem with variable processing speed(DHPFSP-VPS),considering both the minimum makespan and total energy consumption(TEC)as objectives.A discrete multi-objective squirrel search algorithm(DMSSA)is proposed to solve the DHPFSPVPS.DMSSA makes four improvements based on the squirrel search algorithm.Firstly,in terms of the population initialization strategy,four hybrid initialization methods targeting different objectives are proposed to enhance the quality of initial solutions.Secondly,enhancements are made to the population hierarchy system and position updating methods of the squirrel search algorithm,making it more suitable for discrete scheduling problems.Additionally,regarding the search strategy,six local searches are designed based on problem characteristics to enhance search capability.Moreover,a dynamic predator strategy based on Q-learning is devised to effectively balance DMSSA’s capability for global exploration and local exploitation.Finally,two speed control energy-efficient strategies are designed to reduce TEC.Extensive comparative experiments are conducted in this paper to validate the effectiveness of the proposed strategies.The results of comparing DMSSA with other algorithms demonstrate its superior performance and its potential for efficient solving of the DHPFSP-VPS problem.
文摘A Wireless Sensors Network (WSN) is an ad-hoc network populated by small hand-held commodity devices, running on batteries called stations or sensors. Often used in hostiles and sometimes unreachable environments, stations are subject to energetic constraints which can significantly decrease the network life time. Permutation routing problem is mainly found in the literature of WSN. This problem occurs when some stations have items that belong either or not to them. The goal is to send each item to its receiver. To solve this problem, several works are presented in the literature. In this paper, we present a new permutation routing protocol for multi-hop wireless sensors network that, compared to recent work in the field is more efficient in terms of conservation of sensors’ energy, which results in a longer life time of the network. Also, contrary to some other routing protocols which assume that the memory of the sensors is infinite, we show that the memory size of the sensors is limited, which in our opinion is more realistic.
基金supported by the Tencent and Shanghai Jiao Tong University Joint Project
文摘The cocktail party problem,i.e.,tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously,is one of the critical problems yet to be solved to enable the wide application of automatic speech recognition(ASR) systems.In this overview paper,we review the techniques proposed in the last two decades in attacking this problem.We focus our discussions on the speech separation problem given its central role in the cocktail party environment,and describe the conventional single-channel techniques such as computational auditory scene analysis(CASA),non-negative matrix factorization(NMF) and generative models,the conventional multi-channel techniques such as beamforming and multi-channel blind source separation,and the newly developed deep learning-based techniques,such as deep clustering(DPCL),the deep attractor network(DANet),and permutation invariant training(PIT).We also present techniques developed to improve ASR accuracy and speaker identification in the cocktail party environment.We argue effectively exploiting information in the microphone array,the acoustic training set,and the language itself using a more powerful model.Better optimization ob jective and techniques will be the approach to solving the cocktail party problem.
