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31results about How to "Realize global search" patented technology
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Workflow execution optimization method based on multi-population genetic algorithm in cloud computing environment
PendingCN111026534ARealize integrated collaborative optimizationComplete search spaceProgram initiation/switchingResource allocationAlgorithmSpecific population
The invention discloses a workflow execution optimization method based on a multi-population genetic algorithm in a cloud computing environment. The workflow execution optimization method comprises the following steps: acquiring information required by execution optimization; calculating a hierarchical value of the task; initializing a contemporary population; decoding the improved contemporary population and calculating a fitness value; performing independent evolution on a plurality of sub-populations, and timely performing communication among the sub-populations; and outputting an executionoptimization result until a termination condition is satisfied. Compared with a traditional method, the methods and strategies of individual random generation based on the hierarchy and the efficiency ratio, integer coding based on topological sorting, serial individual decoding based on the insertion mode, forward and backward individual decoding improvement, multi-population coordinated evolution and the like are designed and adopted; integrated collaborative optimization of resource allocation and task scheduling is realized, the optimization capability is improved, and the overall efficiency of the algorithm is improved.
Owner:ZHEJIANG UNIV OF TECH
Heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method
ActiveCN110928648AQuality improvementImprove schedulingResource allocationEnergy efficient computingGenetics algorithmsCloud workflow
The invention discloses a heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method. The heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization; calculating a sorting value and a hierarchical value of the task; generating a first-stage task scheduling optimization scheme based on a heuristic method of dynamic key task priority scheduling; obtaining a task scheduling optimization scheme of a second stage based on a genetic algorithm; and outputting the scheduling optimization scheme. According to the heuristic and intelligent computing fused cloud workflow segmented online scheduling optimization method, thesegmented scheduling optimization method integrating the heuristic method and the intelligent calculation method is adopted, and the solving time is equal to the solving time of the heuristic method,and the quality of the solution is similar to the quality of the solution solved by the intelligent calculation method, so that the understanding quality is effectively improved on the premise of adapting to real-time online scheduling.
Owner:探循智能科技(杭州)有限公司
Two-stage cloud workflow scheduling optimization method of hybrid heuristic algorithm and genetic algorithm
InactiveCN111090510AComplete search spaceRealize global searchProgram initiation/switchingResource allocationAlgorithmTopological sorting
The invention discloses a two-stage cloud workflow scheduling optimization method of a hybrid heuristic algorithm and a genetic algorithm. The method comprises the following steps: acquiring information required by scheduling; initializing a population based on a load balancing key task priority method; in two stages: in the stage 1, in combination with a heuristic method of key task priority scheduling, rapidly converging an algorithm near an optimal solution through virtual machine allocation list crossover mutation operation, and in the stage 2, carrying out neighborhood expansion search through crossover mutation operation of a virtual machine allocation list and a task scheduling sequence list to find the optimal solution; outputting a scheduling optimization scheme; adopting an integer coding method based on topological sorting and a serial individual decoding method based on an insertion mode in evolution, and using an FBI & D method and an LDI method to improve population. Compared with the method, the search efficiency and the optimization capability are improved.
Owner:ZHEJIANG UNIV OF TECH
Multimode resource limited project scheduling method using hierarchical adaptive intelligent algorithm
ActiveCN111126800AEasy pretreatmentImprove the speed of solvingResourcesGenetic algorithmsAlgorithmTopological sorting
The invention discloses a multimode resource limited project scheduling method using a hierarchical adaptive intelligent algorithm. The multimode resource limited project scheduling method comprises the following steps: acquiring information required by scheduling optimization; judging whether a feasible scheme exists or not; carrying out pretreatment; calculating a hierarchical value of a task; initializing a contemporary population; improving the contemporary population to calculate individual fitness values; selecting a group of genetic manipulation to perform crossover mutation operation on the current population to form a new population; improving the new population to calculate an individual fitness value, and updating the fitness value of genetic manipulation; forming a new contemporary population by the contemporary population and the new population; and outputting a scheduling optimization result until an iteration termination condition is satisfied. The technical methods of layering, self-adaption, topological sorting, plug-in serial decoding, FBI & D and the like are adopted, the optimization capacity and the search efficiency of the algorithm are improved, and the method is suitable for solving large-scale problems.
Owner:ZHEJIANG UNIV OF TECH
Cloud workflow virtual machine configuration and task scheduling collaborative optimization method
PendingCN110968428AImprove execution performanceRealize integrated collaborative optimizationProgram initiation/switchingResource allocationTopological sortingCloud workflow
The invention discloses a cloud workflow virtual machine configuration and task scheduling collaborative optimization method, comprising the following steps: acquiring information required for executing optimization; calculating a hierarchical value of the task; initializing a contemporary population; decoding, improving and calculating a fitness value; performing independent evolution on a plurality of sub-populations, and timely performing communication among the sub-populations; and until a termination condition is satisfied, outputting an execution optimization result. Compared with a traditional method, the cloud workflow virtual machine configuration and task scheduling collaborative optimization method adopts the methods and strategies based on topological sorting and continuous biased coding, initial individual generation based on hierarchy and benefit ratio, serial individual decoding based on an insertion mode, forward and backward individual decoding and improvement, multi-population coordinated evolution and the like, can realize integrated collaborative optimization of resource allocation and task scheduling, and meanwhile can effectively improve the overall efficiencyof the algorithm.
Owner:ZHEJIANG UNIV OF TECH
Cloud workflow scheduling optimization method based on hierarchical adaptive intelligent calculation algorithm
InactiveCN111047183AComplete search spaceRealize global searchResourcesAlgorithmTheoretical computer science
The invention discloses a cloud workflow scheduling optimization method based on a hierarchical adaptive intelligent calculation algorithm. The method comprises the following steps: obtaining scheduling information; calculating a sorting value and a hierarchical value of the task; preferentially initializing a contemporary population and elite individuals based on the hierarchy and the key activities; performing evolution: forming a new population through improved adaptive crossover mutation operation, improving the new population by adopting FBI & D and LDI methods, calculating a fitness value, and performing elite replacement and storage until an evolution termination condition is met; outputting a scheduling scheme corresponding to the elite individual; according to the method, global search can be realized from the vicinity of a better individual meeting hierarchical coding, and the efficiency of the algorithm is improved by dynamically selecting a proper genetic operation and adopting methods and strategies such as serial individual decoding based on an insertion mode, FBI & D, LDI and elite replacement and storage.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Multimode resource limited project scheduling optimization method adopting two-stage genetic algorithm
ActiveCN110991917AAdd a preprocessing stepReduce problem sizeResourcesSystems intergating technologiesGenetics algorithmsMutation (genetic algorithm)
The invention discloses a multimode resource limited project scheduling optimization method adopting a two-stage genetic algorithm. The multimode resource limited project scheduling optimization method comprises the following steps: acquiring information required by scheduling; judging whether a feasible scheme exists or not; carrying out pretreatment; calculating a hierarchical value of a task; initializing a contemporary population; carrying out evolution in two stages: in stage 1, a scheduling sequence crossover mutation operation based on hierarchy is adopted, an individual execution modelist is generated based on an earliest task completion time, and calculating the fitness value of the individual execution mode list such that the algorithm quickly converges near an optimal solution,in stage 2, an execution mode and a scheduling sequence are adopted for crossover mutation operation, an FBI& D method is used for improving a population calculation fitness value, and neighborhood expansion search is carried out to find the optimal solution; and outputting a scheduling optimization result. Compared with a single-stage search strategy, the method has higher search efficiency andoptimization capability.
Owner:ZHEJIANG UNIV OF TECH
Workflow execution optimization method based on distributed estimation algorithm in cloud computing environment
InactiveCN111026533ARealize integrated collaborative optimizationComplete search spaceProgram initiation/switchingSoftware simulation/interpretation/emulationAlgorithmTheoretical computer science
The invention discloses a workflow execution optimization method based on a distributed estimation algorithm in a cloud computing environment. The workflow execution optimization method comprises thefollowing steps: acquiring information required by execution optimization; calculating a hierarchical value of the task; initializing a contemporary population; decoding the improved contemporary population, calculating the fitness value of the improved contemporary population, and carrying out optimal individual storage; constructing an elitist population, updating a probability model, sampling the probability model to generate a new contemporary population, and outputting an optimization result until a termination condition is met. Compared with a traditional method, the method has the advantages that topological sorting and continuous biased coding, initial individual generation based on hierarchy and benefit ratio, serial individual decoding based on an insertion mode, individual improvement based on forward and backward and new individual generation based on sampling are adopted; and the optimization capability and the search efficiency of the algorithm are improved through methods and strategies such as optimal individual storage.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Workflow scheduling optimization method based on multi-stage genetic algorithm in cloud computing environment
ActiveCN110941487AComplete search spaceRealize global searchProgram initiation/switchingResource allocationTheoretical computer scienceTopological sorting
The invention discloses a workflow scheduling optimization method based on a multi-stage genetic algorithm in a cloud computing environment. The workflow scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization, calculating a task hierarchy value, and initializing a contemporary population and elite individuals; carrying out adaptive evolution in three stages, wherein the heuristic method based on the earliest task completion time in the first stage and the second stage and the task scheduling sequence list genetic operation basedon hierarchy and topological sorting enable the algorithm to converge near the optimal solution as soon as possible, and the genetic operation of a virtual machine allocation list and the task scheduling sequence list is adopted for neighborhood expansion search in the third stage; and meanwhile, a serial decoding method based on an insertion mode, an elitist saving mechanism and an FBI & D and LDI improvement strategy are adopted in evolution. Compared with a single-stage search algorithm, the method has better search efficiency and optimization capability.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Cloud workflow scheduling optimization method using two-dimensional two-stage intelligent computing algorithm
InactiveCN110990125AComplete search spaceRealize global searchResource allocationArtificial lifeCode spaceTheoretical computer science
The invention discloses a cloud workflow scheduling optimization method using a two-dimensional two-stage intelligent computing algorithm. The cloud workflow scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization; calculating a task hierarchy value; initializing a contemporary population based on the hierarchy; carrying out intelligent adaptive evolution in two stages: in stage 1, carrying out searching in individuals meeting hierarchical coding to enable an algorithm to converge near an optimal solution as soon as possible, andin stage 2, adopting global-based extended searching on the basis of stage 1 to find a better solution; outputting a scheduling optimization scheme; and meanwhile, adopting a two-dimensional integer coding method for performing topological sorting on a task scheduling sequence according to a virtual machine, a serial decoding method based on an insertion mode and an FBI&D and LDI improvement strategy in evolution. According to the method, the coding space can be effectively reduced, and meanwhile, compared with a single-stage method, the searching efficiency is higher.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
A Multimodal Resource Constrained Project Scheduling Method Using Hierarchical Adaptive Intelligence Algorithms
ActiveCN111126800BEasy pretreatmentImprove the speed of solvingResourcesGenetic algorithmsAlgorithmSpecific population
Owner:ZHEJIANG UNIV OF TECH
Multimode resource limited project scheduling optimization method based on multi-decoding intelligent algorithm
InactiveCN111062528AAdd a preprocessing stepReduce problem sizeForecastingArtificial lifeDecodesOperations research
The invention discloses a multimode resource limited project scheduling optimization method based on a multi-decoding intelligent algorithm. The multimode resource limited project scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization; judging whether a feasible scheme exists or not; carrying out pretreatment; calculating a static sorting value rank of the task; initializing a contemporary population; carrying out individual updating by adopting heuristic decoding of dynamic key task priority scheduling; adopting an FBI & D method to improve the contemporary population; performing crossover mutation operation to form a new population; carrying out individual decoding and updating on the new population by adopting a multi-decoding strategy; adopting an FBI & D method to improve the new population; forming a new contemporary population by the contemporary population and the new population; and outputting a scheduling optimization result until an evolution termination condition is satisfied. According to the method, various decoding strategies are adopted, so that a better scheduling scheme can be found, and the method has higher search efficiency and optimization capability.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Cloud Workflow Scheduling Optimization Method Based on Hierarchical and Load Balancing Genetic Algorithm
ActiveCN110928671BComplete search spaceRealize global searchProgram initiation/switchingResource allocationAlgorithmTheoretical computer science
The invention discloses a cloud workflow scheduling optimization method based on a hierarchical and load balancing genetic algorithm, comprising the following steps: obtaining information required for scheduling optimization; calculating the hierarchical value of a task; initializing a contemporary population; Population, perform mutation operation on the new population, decode and calculate the fitness value and improve it by means of hierarchy and load balancing, select N different individuals from the contemporary and new populations to form a new contemporary population, until the evolution termination condition is met; Output scheduling optimization scheme. On the premise of realizing global search, the invention adopts serial individual decoding based on insertion mode, individual improvement based on hierarchy and load balancing, parameterized uniform crossover based on preference and other methods to improve the optimization ability and search efficiency of the algorithm.
Owner:ZHEJIANG UNIV OF TECH
Multimodal resource-constrained project scheduling method based on two-dimensional multi-population genetic algorithm
ActiveCN111027856BEasy pretreatmentImprove the speed of solvingResourcesGenetic algorithmsLocal optimumDecoding methods
The invention discloses a multi-mode resource-constrained project scheduling method based on a two-dimensional multi-swarm genetic algorithm, comprising the following steps: obtaining information required for scheduling optimization; judging whether there is a feasible solution; performing preprocessing; Hierarchical value; initialize the contemporary population; use the FBI&D method to improve the individuals in the initial contemporary population and calculate their fitness value; divide into several sub-populations for independent evolution; and communicate between sub-populations in a timely manner; output scheduling optimization results until the evolution termination conditions are met . The invention adopts the two-dimensional integer encoding method based on topological sorting, the serial individual decoding method based on the insertion mode, and the multi-group coordinated evolution mechanism, which can effectively prevent each sub-group from entering the local optimum and premature maturity, and accelerate the convergence, thereby improving the overall Algorithmic efficiency.
Owner:ZHEJIANG UNIV OF TECH
Cloud workflow segmentation online scheduling optimization method integrating heuristic and intelligent computing
ActiveCN110928648BQuality improvementImprove schedulingResource allocationEnergy efficient computingHeuristicsGenetics algorithms
The invention discloses a cloud workflow segmented online scheduling optimization method integrating heuristics and intelligent computing, which comprises the following steps: obtaining information required for scheduling optimization; calculating the sorting value and level value of tasks; The heuristic method generates the task scheduling optimization scheme of the first stage; obtains the task scheduling optimization scheme of the second stage based on the genetic algorithm; outputs the scheduling optimization scheme. The present invention adopts a segmented scheduling optimization method that combines heuristics and intelligent computing, and the solution time is equal to that of the heuristic method, while the quality of the solution is similar to that obtained by the intelligent computing method. Therefore, the present invention adapts to real-time online scheduling. Effectively improve the quality of understanding under the premise.
Owner:探循智能科技(杭州)有限公司
Energy-aware cloud workflow scheduling optimization method based on multi-population genetic algorithm
ActiveCN110928669BComplete search spaceRealize global searchProgram initiation/switchingEnergy efficient computingAlgorithmTopological sorting
The invention discloses an energy consumption perception cloud workflow scheduling optimization method based on a multi-swarm genetic algorithm, comprising the following steps: obtaining information required for scheduling optimization; calculating task level values; initializing populations based on levels; Calculate the fitness value; communicate between subpopulations; each subpopulation evolves independently: perform crossover and mutation operations based on two-dimensional topological sorting to form a new subpopulation, use the FBI&D method to improve the new subpopulation and calculate the fitness value. The new sub-population forms a new contemporary sub-population; until the termination condition is satisfied, the scheduling optimization scheme is output. The invention considers energy consumption factors and adopts multi-group co-evolution strategy, which can effectively prevent the population from entering local optimum and premature maturity, and accelerate the convergence, thereby improving the efficiency of the entire algorithm.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Cloud workflow scheduling optimization method using two-dimensional fixed-length coding intelligent computing algorithm
InactiveCN110991758AComplete search spaceRealize global searchForecastingOffice automationDecoding methodsTheoretical computer science
The invention discloses a cloud workflow scheduling optimization method using a two-dimensional fixed-length coding intelligent computing algorithm. The method comprises the following steps: acquiringinformation required by scheduling optimization; calculating a sorting value rank of the tasks; initializing a contemporary population and carrying out decoding improvement on the contemporary population; performing crossover operation to form a new population; performing mutation operation on the new population; performing decoding improvement on the new population; forming a new contemporary population by the contemporary population and the new population until an evolutionary termination condition is satisfied; outputting a scheduling optimization scheme. According to the method, a two-dimensional fixed-length coding method is adopted, global search can be realized, a serial individual decoding method in an insertion mode and a load balancing individual improvement strategy consideringtransmission time are adopted in evolution, a set of simple and effective crossover mutation method is designed, and the optimization capability and the search efficiency are improved.
Owner:ZHEJIANG UNIV OF TECH
Workflow scheduling optimization method based on random key genetic algorithm in cloud computing environment
InactiveCN111078360AComplete search spaceRealize global searchSoftware simulation/interpretation/emulationGenetic algorithmsLocal optimumReal arithmetic
The invention discloses a workflow scheduling optimization method based on a random key genetic algorithm in a cloud computing environment. The workflow scheduling optimization method comprises the following steps: acquiring information required by scheduling; calculating a sorting value and a hierarchical value of the task; initializing a contemporary population; carrying out evolution: improvingthe contemporary population by adopting FBI & D and LDI methods, calculating fitness values, and forming a new contemporary population by adopting elite retention, individual migration and parameterization uniform crossing until an evolution termination condition is met; and outputting a scheduling optimization scheme. According to the invention, global search can be realized based on random keyreal number coding; the HEFT _ lbt-based individuals are sowed in the initial population, so that the convergence time can be shortened, the individuals can be destroyed to a greater extent by adopting an individual migration method to replace mutation operation, the population diversity is kept, local optimum and premature are avoided, and the solving efficiency and quality are improved.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Workflow optimization method based on partial order adaptive genetic algorithm in cloud computing environment
ActiveCN110928670ARealize integrated collaborative optimizationComplete search spaceProgram initiation/switchingResource allocationAlgorithmTheoretical computer science
The invention discloses a workflow optimization method based on a partial order adaptive genetic algorithm in a cloud computing environment. The workflow optimization method comprises the following steps: acquiring information required for executing optimization; calculating a hierarchical value of the task; initializing a contemporary population; decoding the improved contemporary population andcalculating a fitness value; performing crossover mutation operation on the contemporary population to form a new population; forming a new contemporary population by the contemporary population and the new population; and outputting an execution optimization result until a termination condition is met. According to the workflow optimization method, methods and strategies of initial individual generation based on hierarchy and benefit ratio, adaptive genetic operation, topological sorting, non-decreasing partial order coding, serial individual decoding based on an insertion mode, forward and backward individual decoding improvement and the like are adopted; integrated collaborative optimization of resource allocation and task scheduling is realized; and the optimization capability and thesearch efficiency of the whole algorithm are improved.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Cloud workflow scheduling optimization method adopting multi-population coevolution genetic algorithm
InactiveCN111026506AComplete search spaceRealize global searchProgram initiation/switchingResource allocationLocal optimumDecoding methods
The invention discloses a cloud workflow scheduling optimization method adopting a multi-population coevolution genetic algorithm. The method comprises the following steps: acquiring information required by scheduling optimization; calculating a sorting value rank of the tasks; initializing a population based on key task priority scheduling, the earliest task completion time and a random generation method; performing independent evolution on a plurality of sub-populations, and timely performing communication among the sub-populations; and until a termination condition is satisfied, outputtinga scheduling optimization scheme. According to the method, an integer coding method based on topological sorting, a serial individual decoding method based on an insertion mode, an improved strategy of forward and backward decoding and load balancing and a multi-population coordinated evolution mechanism are designed and adopted, so that each sub-population can be effectively prevented from entering local optimum and premature, the optimization capability is improved, and the overall efficiency of the algorithm is improved.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Workflow scheduling optimization method based on multi-stage genetic algorithm in cloud computing environment
ActiveCN110941487BComplete search spaceRealize global searchProgram initiation/switchingResource allocationTheoretical computer scienceTopological sorting
The invention discloses a workflow scheduling optimization method based on a multi-stage genetic algorithm in a cloud computing environment, comprising the following steps: acquiring information required for scheduling optimization, calculating task level values, initializing contemporary populations and elite individuals; Adaptive evolution: Phase 1 and Phase 2 based on the heuristic method of the earliest completion time of the task and the task scheduling order list based on hierarchical and topological sorting Genetic operations make the algorithm converge to the vicinity of the optimal solution as soon as possible, while Phase 3 adopts the virtual machine allocation list Neighborhood expansion search is carried out with the genetic operation of task scheduling order list; meanwhile, serial decoding method based on insertion mode, elite preservation mechanism, FBI&D and LDI improvement strategies are adopted in evolution. Compared with the single-stage search algorithm, the present invention has better search efficiency and optimization capability.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Energy consumption perception cloud workflow scheduling optimization method based on multi-population genetic algorithm
ActiveCN110928669AComplete search spaceRealize global searchProgram initiation/switchingEnergy efficient computingAlgorithmTopological sorting
The invention discloses an energy consumption perception cloud workflow scheduling optimization method based on a multi-population genetic algorithm. The energy consumption perception cloud workflow scheduling optimization method comprises the following steps: acquiring information required by scheduling optimization; calculating a task hierarchy value; initializing a population based on the hierarchy; adopting an FBI & D method to improve the contemporary population and calculate a fitness value; exchanging among the sub-populations; independently evolving each sub-population: carrying out crossover and mutation operation based on two-dimensional topological sorting to form a new sub-population, improving the new sub-population by adopting an FBI & D method, calculating a fitness value, and forming a new contemporary sub-population by the contemporary sub-population and the new sub-population; and outputting a scheduling optimization scheme until a termination condition is satisfied.The energy consumption perception cloud workflow scheduling optimization method considers energy consumption factors and adopts a multi-population coevolution strategy, can effectively prevent populations from entering local optimum and premature, and can accelerate convergence, so that the efficiency of the whole algorithm is improved.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Multimode resource limited project scheduling method based on two-dimensional multi-population genetic algorithm
ActiveCN111027856AEasy pretreatmentImprove the speed of solvingResourcesGenetic algorithmsLocal optimumDecoding methods
The invention discloses a multimode resource limited project scheduling method based on a two-dimensional multi-population genetic algorithm. The multimode resource limited project scheduling method comprises the following steps: acquiring information required by scheduling optimization; judging whether a feasible scheme exists or not; carrying out pretreatment; calculating a sorting value and a hierarchical value of the task; initializing a contemporary population; adopting an FBI & D method to improve individuals in the initial contemporary population and calculate fitness values of the individuals; performing independent evolution on a plurality of sub-populations; timely carrying out communication among the sub-populations; and outputting a scheduling optimization result until an evolution termination condition is satisfied. According to the method, a two-dimensional integer coding method based on topological sorting, a serial individual decoding method based on an insertion mode and a multi-population coordinated evolution mechanism are adopted, so that each sub-population can be effectively prevented from entering local optimum and precocity, convergence is accelerated, and the efficiency of the whole algorithm can be improved.
Owner:ZHEJIANG UNIV OF TECH
Cloud workflow scheduling optimization method based on two-dimensional coding genetic algorithm
InactiveCN111078381AComplete search spaceRealize global searchProgram initiation/switchingResource allocationAlgorithmCode space
The invention discloses a cloud workflow scheduling optimization method based on a two-dimensional coding genetic algorithm. The method comprises the following steps: obtaining scheduling information;calculating a task hierarchy value; initializing a contemporary population based on the hierarchy and the number of sub-tasks; carrying out evolution; adopting an FBI & D method and an LDI method toimprove a contemporary population and calculate a fitness value; carrying out parameterized uniform crossover operation based on preference and two-dimensional topological sorting to form a new population; carrying out mutation operation on the new population, improving the new population by adopting FBI & D and LDI methods, calculating a fitness value, and selecting N different individuals from the contemporary population and the new population to form a new contemporary population until an evolution termination condition is met; and outputting a scheduling optimization scheme. The two-dimensional integer coding method adopted by the invention can reduce the coding space of the algorithm and improve the solving efficiency and quality.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
Cloud workflow scheduling optimization method based on hierarchy and load balancing genetic algorithm
ActiveCN110928671AComplete search spaceRealize global searchProgram initiation/switchingResource allocationAlgorithmTheoretical computer science
The invention discloses a cloud workflow scheduling optimization method based on a hierarchy and load balancing genetic algorithm. The cloud workflow scheduling optimization method comprises the following steps: obtaining information required by scheduling optimization; calculating a hierarchical value of the task; initializing a contemporary population; carrying out evolution: carrying out crossover operation to form a new population, carrying out mutation operation on the new population, carrying out decoding to calculate a fitness value, carrying out improvement by using a hierarchy and load balancing method, and selecting N different individuals from the contemporary population and the new population to form a new contemporary population until an evolution termination condition is met;and outputting a scheduling optimization scheme. According to the cloud workflow scheduling optimization method, on the premise that global search is achieved, serial individual decoding based on aninsertion mode, individual improvement based on hierarchy and load balancing, parameterization uniform crossing based on preference and other methods are adopted, and the optimization capacity and search efficiency of the algorithm are improved.
Owner:ZHEJIANG UNIV OF TECH
Workflow scheduling optimization method based on multi-decoding genetic algorithm in cloud computing environment
InactiveCN111061563AComplete search spaceRealize global searchResource allocationArtificial lifeAlgorithmTheoretical computer science
The invention discloses a cloud workflow scheduling optimization method based on a multi-decoding genetic algorithm in a cloud computing environment. The cloud workflow scheduling optimization methodcomprises the following steps: acquiring information required by scheduling; calculating a task hierarchy value; initializing a contemporary population; carrying out evolution; forming a new population by adopting crossover operation and carrying out mutation operation; carrying out individual decoding and updating based on serial individual decoding of an insertion mode, heuristic decoding of theearliest task completion time and heuristic decoding of dynamic key task priority scheduling; improving the new population by adopting FBI & D and LDI methods, forming a next-generation population bythe contemporary population and the new population, and enabling the next-generation population to be a contemporary population until an evolution termination condition is met; and outputting a scheduling scheme corresponding to the best individual in the contemporary population. Compared with a single decoding strategy, the method has higher efficiency and optimization capability, and the solving quality can be improved.
Owner:ZHEJIANG UNIV OF TECH
Resource-constrained project scheduling optimization method based on layered two-stage intelligent algorithm
ActiveCN110942209AComplete search spaceRealize global searchForecastingGenetic algorithmsIntelligent algorithmsRapid convergence
The invention discloses a resource-constrained project scheduling optimization method based on a layered two-stage intelligent algorithm. The method comprises the following steps: acquiring information required by scheduling optimization; calculating a sorting value and a hierarchical value of the task; initializing a contemporary population; calculating a fitness value by adopting serial individual decoding based on an insertion mode; carrying out evolution in two stages, wherein in the first stage, an algorithm is rapidly converged near an optimal solution by adopting a hierarchy-based crossover mutation operation, in the second stage, parameterized uniform crossover operation and mutation operation based on topological sorting are adopted, a population calculation fitness value is improved by adopting an FBI & D method, and neighborhood extension search is carried out to find the optimal solution; outputting a scheduling optimization scheme. Compared with a traditional single-stagesearch strategy, the method has higher search efficiency and optimization capability.
Owner:ZHEJIANG UNIV OF TECH
Scheduling optimization method for multi-mode resource-constrained projects using two-stage genetic algorithm
ActiveCN110991917BAdd a preprocessing stepReduce problem sizeResourcesSystems intergating technologiesCompletion timeAlgorithm
The invention discloses a multi-mode resource-constrained project scheduling optimization method using a two-stage genetic algorithm, which comprises the following steps: obtaining information required for scheduling; judging whether there is a feasible solution; preprocessing; calculating task level values; Population; evolves in two stages: Stage 1 adopts hierarchical-based scheduling sequence crossover mutation operation and generates a list of individual execution modes based on the earliest completion time of the task and calculates its fitness value, so that the algorithm quickly converges to the vicinity of the optimal solution, stage 2 Adopt the execution mode and scheduling sequence list crossover mutation operation and use the FBI&D method to improve the population to calculate the fitness value, carry out the expansion search of the neighborhood to find the optimal solution; output the scheduling optimization result; compared with the single-stage search strategy, the present invention has higher efficiency Search efficiency and optimization capabilities.
Owner:ZHEJIANG UNIV OF TECH
High-dimensional multi-objective optimization design method for optical thin films
ActiveCN111723528BRealize global searchComprehensive analysis of interrelationshipsArtificial lifeDesign optimisation/simulationGenetics algorithmsEngineering
Owner:长春国科精密光学技术有限公司
Workflow Optimization Method Based on Partial Order Adaptive Genetic Algorithm in Cloud Computing Environment
ActiveCN110928670BRealize integrated collaborative optimizationComplete search spaceProgram initiation/switchingResource allocationAlgorithmTheoretical computer science
The invention discloses a workflow optimization method based on a partial order self-adaptive genetic algorithm in a cloud computing environment. Fitness value; cross-mutation operation is performed on the contemporary population to form a new population; a new contemporary population is formed from the contemporary population and the new population; until the termination condition is met, the optimization result is output; the present invention uses the initial individual generation and adaptation based on the level and benefit ratio Methods and strategies such as genetic operations, topological sorting, non-decreasing partial order coding, serial individual decoding based on insertion mode, forward-backward individual decoding improvement, etc., realize integrated collaborative optimization of resource allocation and task scheduling, and improve the efficiency of the entire algorithm Optimizing ability and search efficiency.
Owner:ZHEJIANG GONGSHANG UNIVERSITY
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