31results about How to "Complete search space" patented technology

The invention discloses a multi-mode resource-constrained project scheduling method using a layered adaptive intelligent algorithm, comprising the following steps: obtaining information required for scheduling optimization; judging whether there is a feasible solution; performing preprocessing; Initialize the contemporary population; improve the contemporary population to calculate the individual fitness value; select a set of genetic operations to perform crossover mutation operation on the contemporary population to form a new population; improve the new population to calculate the individual fitness value, and update the fitness value of the genetic operation; The population forms a new contemporary population; until the iteration termination condition is met, the scheduling optimization result is output. The invention adopts technical methods such as layering, self-adaptation, topological sorting, inserting serial decoding, FBI&D, etc., which improves the optimization ability and search efficiency of the algorithm, and is suitable for solving large-scale problems.

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.

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.

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.

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.

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.

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.

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.