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Batching optimization method based on new multi-objective artificial bee colony algorithm

A technology of artificial bee colony algorithm and optimization method, applied in calculation, calculation model, biological model, etc.

Inactive Publication Date: 2017-02-01
SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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Problems solved by technology

[0004] Aiming at the defects exposed by existing calculations when solving large-scale, non-linear, complex batching optimization problems with multiple objectives, the present invention proposes a new multiple Batching Optimization Method Based on Objective Artificial Bee Colony Algorithm

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  • Batching optimization method based on new multi-objective artificial bee colony algorithm
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  • Batching optimization method based on new multi-objective artificial bee colony algorithm

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Embodiment Construction

[0058] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0059] The present invention proposes a batching optimization method of a novel multi-objective artificial bee colony algorithm that draws lessons from the cooperative foraging behavior of multiple hive bees in nature, and the idea of ​​its solution algorithm is as follows: figure 1 shown.

[0060] Step 1: Parameter initialization

[0061] 1.1) The real number encoding is carried out in the form of a 2×(n+1) matrix, and the column number of the matrix is ​​the length of the chromosome. The first row of chromosomes represents the amount X of raw materials; the second row of chromosomes represents the smelting time T of raw materials 0 and the smelting time t from the melt Q . The n+1th integer of the chromosome in the feeding amount line can correspond to the melt constant Q, which can be skipped in the cycle calculation, and the matrix for...

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Abstract

The invention relates to a batching optimization method based on a new multi-objective artificial bee colony algorithm. The method comprises steps: 1, batching parameters are initialized; 2, a batching optimization multi-objective model is built, and a fitness value is evaluated; 3, data are updated; 4, after preset algebra optimization, each colony selects partial individuals with excellent information for information exchange, the selected individuals form one list, the list is transmitted to another colony, each colony needs to prepare one repalcement list, and individuals in the list are replaced by individuals from other colonies; and 5, if a preset ending condition is not achieved, the second step is returned, and if an iterative termination condition is achieved, calculation is stopped, and a result is outputted finally. The method of the invention has the advantages that the global search capability is strong; the convergence rate is quick; the solution convergence precision is high; the solution distribution is uniform; the solution comprehensive performance is excellent; various feasible batching schemes can be provided with only one-time operation; and control and management on the batching process by a batching person are facilitated.

Description

technical field [0001] The invention relates to a batching optimization method based on a novel multi-objective artificial bee colony algorithm, which belongs to the field of non-ferrous metal manufacturing and also relates to the field of swarm intelligence algorithms. Background technique [0002] For the batching process of the high-precision copper strip production line, there are many influencing factors and they are interrelated. In addition to the ratio within the process range to ensure product quality, raw material costs, feeding sequence, inventory, melt and raw materials in the smelting process must also be considered. Factors such as the burning loss in the medium and the maximum reuse of waste materials. The batching process with the above characteristics contains a large amount of uncertain information. It is difficult to describe the correlation of the influencing factors in the process by using the traditional linear model to model it, and it cannot reasonabl...

Claims

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Application Information

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IPC IPC(8): G06Q50/04G06N3/00
CPCY02P90/30
Inventor 朱云龙张浩张丁一
Owner SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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