31results about How to "Realize global search" patented technology

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.