Meme evolution multiobjective optimization scheduling method based on objective importance decomposition

Abstract

The invention relates to a meme evolution multiobjective optimization scheduling method based on objective importance decomposition, comprising following steps: randomly generating an initial population with volume of N; in every generation of the algorithm, selecting by the current population through binary championship, generating an offspring population by a genetic operator; carrying out fine search to the offspring population by a partial search strategy to obtain improved population; combining the current population, the offspring population and the improved population to generate a population, carrying out mutation operation to the individuals in the population with identical objective size; sorting the individuals in the population through using the rapid nondominated sorting and crowding distance method in the NSGA-II (Nondominated Sorting Genetic Algorithm II), thus selecting N best solutions as the next generation populations. According to the invention, a multiobjective flexible working shop system can be scheduled effectively; the scheduling effect is superior to the existing advanced algorithm; and the method of the invention can be widely applied in the computer application technical field and the production scheduling field.

Description

technical field [0001] The invention relates to a multi-objective optimal scheduling method, in particular to a memetic evolution multi-objective optimal scheduling method applied in the fields of computer application technology and production scheduling based on objective importance decomposition. Background technique [0002] The second-generation non-dominated genetic algorithm (NondominatedSortingGeneticAlgorithmII, NSGA-II) first randomly generates an initial population containing N individuals, where N is the population size. Next, the algorithm iterates until the termination condition is met. In generation t, the algorithm generates offspring population Qt through random selection, Simulated Binary Crossover (SBX) and polynomial mutation based on the current population Pt. Both Pt and Qt are of size N. Therefore, the merging of two populations Pt and Qt will form a new population Rt=Pt∪Qt with a population size of 2N. In order to select the best N solutions from th...

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Problems solved by technology

[0004] NSGA-II often does not make full use of problem-related knowledge when solving multi-objective flexible job shop scheduling problems, and the exploration ability of local search is limited; MOGLS focuses on how to select solutions for local search in multi-objective optimization, and how to solve local search problems. The problem of evaluating the solution, but the global search ability is lacking

[0005] In addition, the common defects of these two classic algorithms: first, they do not make full use of the relevant knowledge of the problem; second, they do not consider the difference in importance between multiple objectives of the problem and the correlation between them.

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