CN109597304A - Die storehouse Intelligent partition storage method based on artificial bee colony algorithm - Google Patents
Die storehouse Intelligent partition storage method based on artificial bee colony algorithm Download PDFInfo
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Abstract
The invention discloses the die storehouse Intelligent partition storage method based on artificial bee colony algorithm, include the following steps: for mold partitioned storage optimization problem, primarily generality description is carried out to the solution of three energy consumption to be solved, efficiency, quality parts；Energy consumption problem is mainly reflected in: being guaranteed that cargo consumed energy during being transported outbound is smaller, is then needed to guarantee that piler acting is less；Consider efficiency factor, be summarized as follows: the high mold of storage frequency is distributed to out in position only close apart from shelf access adit as far as possible, could reduce the overall operation time of piler, and that improves mold goes out warehouse-in efficiency；The bring that analyzes quality factors is inconvenient, can summarize are as follows: to optimize to goods yard, decline the center of gravity of shelf, achieve the purpose that shelf-stable.
Description
Technical field
The invention belongs to intelligence manufactures to automate stamping line field, and in particular to a kind of based on artificial bee colony algorithm
Die storehouse Intelligent partition memory technology.
Background technique
Currently, mold has that stacking even superposition everywhere stacks, and perplexs to mold lookup and conveyer belt；Mold
The mode using folding vehicle or crane is transported, the shipping time is long, and especially large-scale, heavy dies transport process must be carefully slow,
The time of die change is reduced not from source；In mold storage management aspect, most enterprises still use electronic table at present
Account cooperates the mode of artificial paper record to be managed mold, and not only inefficiency, error rate are high for this way to manage, but also
Increase the management cost of business manpower, also there are technology disconnections with advanced quick mold change systems.Therefore how high-effect high-quality
It is field a great problem in the urgent need to address that ground, which is managed mold warehouse,.
Artificial bee colony algorithm (artificial bee colony, ABC) was by Turkey scholar Karaboga in 2005
It proposes, it is the gathering honey behavior of simulation honeybee to solve the optimization problem of some multidimensional and multimode in life.Relative to other intelligence
Can be for algorithm, ABC algorithm has many advantages, such as that control parameter is few, be easily achieved and calculates simple, therefore has been widely used in
In a series of problems, such as function optimization and engineering field.For reduction energy consumption most significant in mold storehouse management, improve efficiency,
The solution and optimization for the problems such as improving stability and quality, can be used artificial bee colony algorithm to carry out die storehouse multiple-objection optimization
Partitioning strategies research, then solves model, determines optimal solution, can largely reduce goods by this method
Object consumed energy during being transported outbound improves out warehouse-in efficiency, improves shelf stabilities.
But artificial bee colony algorithm convergence rate, in terms of have some defects, and with other intelligence
Energy algorithm such as ant group algorithm, genetic algorithm is compared with particle swarm algorithm, for the convergence variation of ABC algorithm and grinding for convergence method
Study carefully not enough thoroughly, and the validity of the algorithm and specific aim prove that this largely makes by emulation experiment mostly
The about application and improvement of ABC algorithm.
Summary of the invention
In view of the above shortcomings of the prior art and stamping line mold storing area occupied is big, storage low efficiency, pipe
The problems such as reason is difficult, the present invention is realized preferably to be optimized with energy consumption minimum and working time minimum and shelf stabilities
Target, propose a kind of die storehouse Intelligent partition memory technology based on artificial bee colony algorithm have simple algorithm, strong flexibility and
The advantages that strong robustness.Now adopt the following technical scheme that
To achieve the goals above, the present invention provides a kind of die storehouse Intelligent partition storage skill based on artificial bee colony algorithm
Art includes the following steps:
It, primarily will be to energy consumption to be solved, efficiency, the solution of three parts of quality for mold partitioned storage optimization problem
Certainly scheme carries out generality description；
Energy consumption problem is mainly reflected in: being guaranteed that cargo consumed energy during being transported outbound is smaller, is then needed
Guarantee that piler acting is less；
Consider efficiency factor, be summarized as follows: position only close apart from shelf access adit is distributed to out as far as possible is put in storage frequency
High mold could reduce the overall operation time of piler, and that improves mold goes out warehouse-in efficiency；
The bring that analyzes quality factors is inconvenient, can summarize are as follows: and goods yard is optimized, decline the center of gravity of shelf,
Achieve the purpose that shelf-stable；
Before partitioned storage optimization, to determine the shelf goods lattice type of die storehouse, stack type stereo warehouse be done as follows
Three hypothesis；
Firstly, die storehouse design aspect, the die storehouse goods lattice studied herein uses modular goods lattice, meeting among two rows of shelf
There is a tunnel for piler traveling；
Then, the same sides of shelf is arranged in outbound platform and storage platform, outbound task and inbound task all sometimes, it is convenient
Piler carries out multiple working；
Finally, it is assumed that goods lattice is square；
For assumed above, mathematical model is begun setting up；
Slotting optimization problem will account for from many aspects, not only weigh the turnover rate and correlation of kinds of goods, while
Putting for kinds of goods is set to guarantee that shelf center of gravity is minimum as far as possible；But mutually restricted between three, it is desirable to meet simultaneously, it is necessary to convert
It is solved at multiobjective decision-making；
If (x, y, z) is the goods yard coordinate in warehouse, i.e. xth arranges z layers of y column；The warehouse one shares a and arranges c layers of b column；vxFor heap
The speed of service in the direction stack machine x, vyFor the speed of service in the direction piler y, vzFor the speed of service in the direction piler z；P is heap
For stack machine to the lateral distance of outlet, q is fore-and-aft distance of the piler to outlet, fxyzFor the turnover rate of kinds of goods, mxyzFor kinds of goods
Quality；
Entire optimization process is divided into three phases, that is, obtains the minimum energy dissipation stage, improves out the warehouse-in efficiency stage, improves
The shelf-stable sexual stage；
1) the minimum energy dissipation stage is obtained
For guarantee cargo consumed energy during being transported outbound it is smaller, then need to guarantee piler acting compared with
It is few；Cargo acting is obtained by mechanics principle；
Wherein, g is acceleration of gravity, is known constant；X, y, z are respectively kinds of goods distance yOz in coordinate space,
XOz, the vertical distance of xOy coordinate plane, h are the height value where kinds of goods, coefficient of friction (μ of the μ between piler and cargo
≤ 1), L0For the length of goods yard cell；kxyzIt is known constant for proportionality coefficient；mxyzFor the quality of kinds of goods；
Coefficient of friction between piler and cargo is smaller (μ≤1), and the function that cargo is laterally done is compared to the function longitudinally done
It can be ignored；According to above-mentioned model it is found that g is known amount；Z is for kinds of goods apart from xOy coordinate plane in coordinate space
Vertical distance, h be kinds of goods where height value；kxyzIt is known constant for proportionality coefficient；mxyzFor the quality of kinds of goods, because
This available following objective function
2) the warehouse-in efficiency stage is improved out
It is most short in order to make to transport distance, need for the high cargo of outbound frequency to be placed on the inner closer position in outlet；It improves
Efficiency, it is necessary to keep the outbound frequency of cargo and the cargo-shipping Time sum of products minimum；Assuming that fxyzFor the outbound frequency of cargo
Rate, and introduce txyzTo indicate cargo-shipping Time, therefore construction such as minor function；
Wherein, p=x-1, q=1, L0For goods yard unit length, 1 can be considered, be known quantity；Remaining symbol is above-mentioned
It is described in detail in model, does not make redundancy description herein.
3) improve the shelf-stable sexual stage
Shelf-stable or not it is the height of its center of gravity；The bottom that heavier cargo is placed in shelf be will increase into whole goods
The stability of frame, so that the distance on the focus point of shelf to ground is most short；According to the load-bearing principle of shelf " lightness sensation in the upper and heaviness in the lower ", need
Guarantee that the sum of products of the cargo mass on each goods yard and layer where it is minimum；
Therefore following objective function is constructed；
It is as follows that slotting optimization object and multi object mathematical model can to sum up be obtained；
The object and multi object mathematical model of slotting optimization is established to reduce energy consumption and improve outbound efficiency as main target, goods
On the basis of frame stability is as by-end；It is mutually restricted between each objective function, individually optimizing research can not solve
Problem；It needs to assign different weights to each objective function by the Exchanger Efficiency with Weight Coefficient Method of genetic algorithm, which is become to be conducive to
The single-goal function problem of calculating；
It is as follows to construct multiple objective function；
Min f=w1f1+w2f2+w3f3 (6)
Wherein < w1< 1,0 < w2< 1,0 < w3< 1, and w1+w2+w3=1, w1,w2,w3For weight coefficient, assigned according to practical problem
Give different values；
Mathematical model foundation finishes；
It is then determined decision variable and constraint condition；
Decision and constraint should include the definition of constrained domain and two side of unique constraints of coordinate in the optimization problem of this paper
Face；
1) definition of constrained domain；
Mold is optimized in fixed area, and position coordinates must belong in optimization region；This paper mould optimization
Region be that a arranges c layers of b column of shelf area；It will be denoted as the 1st row apart from a nearest row for access adit, nearest 1 is classified as the 1st column,
The bottom is the 1st layer；So the decision variable of this paper is x, y, z；Constraint condition is 1≤x≤a, 1≤y≤b, 1≤z≤c；
2) unique constraints of coordinate；
A mold can only be stored on one goods yard, therefore the storage goods yard of a mold there can only be one, not allow
Existing duplicate coordinate, so another decision variable is kxyz；
Constraint condition is；
So far, the work of mold partitioned storage optimization mathematical modeling has been completed；
Model above is solved；
Firstly, in conjunction with the basic principle of artificial bee colony algorithm；
Artificial bee colony is divided into 3 classes by simulating the gathering honey mechanism of practical honeybee by the ABC algorithm of standard: being led bee, is followed
Bee and search bee；
The target of entire bee colony is to find the maximum nectar source of nectar amount；
Gathering honey bee shares nectar source information using the new nectar source of previous nectar source information searching and with observation bee；Bee is observed in bee
The new nectar source of information searching for waiting in room and sharing according to gathering honey bee；The task of investigation bee be find one it is new valuable
Nectar source, they randomly find nectar source near honeycomb；
Using ABC algorithm when solving mold partitioned storage optimization problem；
The position in goods yard is abstracted into the point in solution space, represents the potential solution of problem, goods yard i (i=1,2,
NP quality) corresponds to the fitness value f of solutioniti, NP is the quantity in goods yard；
Cargo is divided by ABC algorithm to be led bee, follows 3 seed type of bee and search bee, wherein leading bee and bee being followed respectively to account for
The half of cargo total amount, quantity is equal to the quantity in goods yard, and there was only one in the same time of every goods yard and lead bee on goods yard；
Assuming that the dimension of Solve problems is D, in t iteration, the position of goods yard i is expressed as x 'i={ x 'i1,x
′i2,···x′iD, wherein t indicates current the number of iterations；xid∈[Ld,Ud],LdAnd UdIt respectively indicates under search space
Limit and the upper limit, d=1,2, D；
The initial position of goods yard i is randomly generated in all goods yards according to described above；
xid=Ld+rand(0,1)(Ud-Ld)； (8)
In the search incipient stage；
It leads bee to be searched for around the L of goods yard according to formula and generates a new goods yard；
In formula: d is a random integers in [1, D], and expression leads bee to be randomly chosen one-dimensional scan for；j∈
{ 1,2, NP } indicates to randomly choose the goods yard for being not equal to i in NP goods yard；It is that [- 1,1] are equally distributed
Random number determines perturbation amplitude (magnitude of the perturbation)；
As new goods yard Vi=[vi1,vi2,···vid] fitness be better than XiWhen, using the method V of greediness selectioniGeneration
For Xi, otherwise retain Xi；
After all operations for leading bee perfect (9), goods yard information is shared in the information interchange area that flies back；
It follows bee according to the goods yard information for leading bee to share, is followed by the probability that formula (10) calculate；
It follows bee to lead bee using the method choice of roulette, i.e., generates an equally distributed random number r in [0,1],
If piGreater than r, this follows bee to generate a new goods yard around the i of goods yard, and leads the identical greedy selection of bee using same
Method determine retain goods yard；
The stage in search process；
If goods yard XiBy trial iterative search arrival threshold value limit without finding better goods yard, the goods yard
XiIt will be abandoned, it is corresponding to lead bee diversification in role for search bee；
A new goods yard will be randomly generated instead of X in search space in search beei, the above process such as formula (11)；
For without loss of generality, by taking the optimization problem of minimum as an example, in ABC algorithm, the fitness evaluation of solution is according to formula
(12) it calculates；
Secondly, starting artificial bee colony algorithm process, it is divided into following steps progress；
Step 1: each goods yard X is initializedi；Setup parameter NP, limit and maximum number of iterations；T=1；
Step 2: for goods yard XiDistribution one leads bee, scans for by formula (9), generates new goods yard Vi；
Step 3: V is evaluated according to formula (12)iFitness, according to greediness selection method determine retain goods yard；
Step 4: the probability that the goods yard for leading bee to find is followed is calculated by formula (10)；
Step 5: following bee to scan for using mode identical with bee is led, and is determined and is protected according to the method for greediness selection
The goods yard stayed；
Step 6: judge goods yard XiWhether the condition that satisfaction is abandoned, if it is satisfied, lead bee be converted into investigation bee, it is no
Then pass directly to step 8；
Step 7: new goods yard is randomly generated according to formula (11) in investigation bee；
Step 8: t=t+1 judges whether to meet termination condition, if so, output most solves, otherwise goes to step two, ask
Solution preocess terminates；
Finally, present invention combination MATLAB schemes the objective function of foundation into the implementation phase of artificial bee colony algorithm
Shape shows, and realizes the visualization of calculated result and programming.
The invention has the advantages that；
The method of the present invention is stored in energy consumption and working time and shelf-stable for solving die storehouse Intelligent partition
Property optimization solution, this method establishes objective function based on the arrangement in goods yard in die storehouse and cargo, and combines MATLAB software
The visualization processing of figure has been carried out to function, has had many advantages, such as that intuitive is strong, result is reliable.Three sections addressed through the invention
Formula resolution policy can enhance the search capability of artificial bee colony algorithm, improve convergence speed of the algorithm and convergence precision, to simplify
The intelligent process of die storehouse partitioned storage, improves the speed and precision of solution.
Detailed description of the invention
Fig. 1 is the goods yard distribution condition schematic diagram before optimizing in the present invention.
Fig. 2 is the goods yard distribution condition schematic diagram one after optimizing in the present invention.
Fig. 3 is iteration and current relation schematic diagram in the present invention.
Fig. 4 is the implementation flow chart of this method.
Specific embodiment
Below in conjunction with drawings and examples, the present invention is described in detail.
Fig. 1 is the goods yard distribution condition schematic diagram before optimizing in the present invention.Fig. 2 is the goods yard distribution after optimizing in the present invention
Situation schematic diagram.Fig. 3 is iteration and current relation schematic diagram in the present invention.Fig. 4 is the implementation flow chart of this method.
The method of the present invention is stored in energy consumption and working time and shelf-stable for solving die storehouse Intelligent partition
Property optimization solution, this method establishes objective function based on the arrangement in goods yard in die storehouse and cargo, and combines MATLAB software
The visualization processing of figure has been carried out to function, has had many advantages, such as that intuitive is strong, result is reliable.Three sections addressed through the invention
Formula resolution policy can enhance the search capability of artificial bee colony algorithm, improve convergence speed of the algorithm and convergence precision, to simplify
The intelligent process of die storehouse partitioned storage, improves the speed and precision of solution.
Claims (5)
1. the die storehouse Intelligent partition storage method based on artificial bee colony algorithm, it is characterised in that: excellent for mold partitioned storage
Change problem primarily will carry out generality description to the solution of three energy consumption to be solved, efficiency, quality parts；
Energy consumption problem is embodied in: being guaranteed that cargo consumed energy during being transported outbound is smaller, is then needed to guarantee heap
Stack machine acting is less；
Consider efficiency factor, be summarized as follows: it is high that storage frequency is distributed to out in position only close apart from shelf access adit as far as possible
Mold could reduce the overall operation time of piler, and that improves mold goes out warehouse-in efficiency；
The bring that analyzes quality factors is inconvenient, summarizes are as follows: to optimize to goods yard, decline the center of gravity of shelf, reach shelf
Stable purpose；
Before partitioned storage optimization, to determine the shelf goods lattice type of die storehouse, following three is done to stack type stereo warehouse
Assuming that；
Firstly, die storehouse design aspect, die storehouse goods lattice uses modular goods lattice, has one for piler among two rows of shelf
The tunnel of traveling；
Then, the same sides of shelf is arranged in outbound platform and storage platform, outbound task and inbound task all sometimes, facilitate stacking
Machine carries out multiple working；
Finally, it is assumed that goods lattice is square.
2. the die storehouse Intelligent partition storage method according to claim 1 based on artificial bee colony algorithm, it is characterised in that:
For assumed above, mathematical model is begun setting up；
Slotting optimization problem will account for from many aspects, not only weigh the turnover rate and correlation of kinds of goods, while also to use up
Putting for kinds of goods may be made to guarantee that shelf center of gravity is minimum；But mutually restricted between three, it is desirable to meet simultaneously, it is necessary to be converted to more
Objective decision solves；
If (x, y, z) is the goods yard coordinate in warehouse, i.e. xth arranges z layers of y column；The warehouse one shares a and arranges c layers of b column；vxFor piler x
The speed of service in direction, vyFor the speed of service in the direction piler y, vzFor the speed of service in the direction piler z；P be piler extremely
The lateral distance of outlet, q are fore-and-aft distance of the piler to outlet, fxyzFor the turnover rate of kinds of goods, mxyzFor the quality of kinds of goods；
Entire optimization process is divided into three phases, that is, obtains the minimum energy dissipation stage, improves out the warehouse-in efficiency stage, improves shelf
Stablize the sexual stage；
1) the minimum energy dissipation stage is obtained
To guarantee that cargo consumed energy during being transported outbound is smaller, then need to guarantee that piler acting is less；
Cargo acting is obtained by mechanics principle；
Wherein, g is acceleration of gravity, is known constant；X, y, z are respectively kinds of goods distance yOz, xOz in coordinate space,
The vertical distance of xOy coordinate plane, height value of the h where kinds of goods, coefficient of friction of the μ between piler and cargo, μ≤1,
L0For the length of goods yard cell；kxyzIt is known constant for proportionality coefficient；mxyzFor the quality of kinds of goods；
Coefficient of friction between piler and cargo is small, and the function that cargo is laterally done can be ignored not compared to the function longitudinally done
Meter；According to above-mentioned model it is found that g is known amount；Z be kinds of goods in coordinate space apart from the vertical distance of xOy coordinate plane,
H is the height value where kinds of goods；kxyzIt is known constant for proportionality coefficient；mxyzFor the quality of kinds of goods, therefore obtain following mesh
Scalar functions
2) the warehouse-in efficiency stage is improved out
It is most short to make to transport distance, need for the high cargo of outbound frequency to be placed on the inner closer position in outlet；It improves efficiency, just
Need to make outbound frequency and the cargo-shipping Time sum of products of cargo minimum；Assuming that fxyzFor the outbound frequency of cargo, and introduce
txyzTo indicate cargo-shipping Time, therefore construction such as minor function；
Wherein, p=x-1, q=1, L0For goods yard unit length, 1 can be considered, be known quantity；
3) improve the shelf-stable sexual stage
Shelf-stable or not it is the height of its center of gravity；The bottom that heavier cargo is placed in shelf be will increase into whole shelf
Stability, so that the distance on the focus point of shelf to ground is most short；According to the load-bearing principle of shelf " lightness sensation in the upper and heaviness in the lower ", need to guarantee
Cargo mass on each goods yard and the sum of products of layer where it are minimum；
Therefore following objective function is constructed；
It is as follows that slotting optimization object and multi object mathematical model can to sum up be obtained；
The object and multi object mathematical model of slotting optimization is established to reduce energy consumption and improve outbound efficiency as main target, and shelf are steady
It is qualitative to be used as on the basis of by-end；It is mutually restricted between each objective function, individually optimizing research can not solve the problems, such as；
It needs to assign different weights to each objective function by the Exchanger Efficiency with Weight Coefficient Method of genetic algorithm, which is become to calculate
Single-goal function problem；
It is as follows to construct multiple objective function；
Minf=w1f1+w2f2+w3f3 (6)
Wherein < w1< 1,0 < w2< 1,0 < w3< 1, and w1+w2+w3=1, w1,w2,w3For weight coefficient, assigned not according to practical problem
Same value；
Mathematical model foundation finishes.
3. the die storehouse Intelligent partition storage method according to claim 1 based on artificial bee colony algorithm, it is characterised in that:
Determine decision variable and constraint condition；
Decision and constraint should include the definition of constrained domain and two aspect of unique constraints of coordinate；
1) definition of constrained domain；
Mold is optimized in fixed area, and position coordinates must belong in optimization region；The area of this paper mould optimization
Domain is the shelf area that a arranges c layers of b column；It will be denoted as the 1st row apart from a nearest row for access adit, nearest 1 is classified as the 1st column, most bottom
Layer is the 1st layer；So the decision variable of this paper is x, y, z；Constraint condition is 1≤x≤a, 1≤y≤b, 1≤z≤c；
2) unique constraints of coordinate；
A mold can only be stored on one goods yard, therefore the storage goods yard of a mold there can only be one, not allow to weigh
Multiple coordinate, so another decision variable is kxyz；
Constraint condition is；
So far, the work of mold partitioned storage optimization mathematical modeling has been completed.
4. the die storehouse Intelligent partition storage method according to claim 4 based on artificial bee colony algorithm, it is characterised in that:
Mold partitioned storage optimization mathematical modeling is solved；
Firstly, in conjunction with the basic principle of artificial bee colony algorithm；
Artificial bee colony is divided into 3 classes by simulating the gathering honey mechanism of practical honeybee by the ABC algorithm of standard: lead bee, follow bee and
Search bee；
The target of entire bee colony is to find the maximum nectar source of nectar amount；
Gathering honey bee shares nectar source information using the new nectar source of previous nectar source information searching and with observation bee；Bee is observed in honeycomb
The new nectar source of information searching for waiting and sharing according to gathering honey bee；The task of investigation bee is to find a new valuable honey
Source, they randomly find nectar source near honeycomb；
Using ABC algorithm when solving mold partitioned storage optimization problem；
The position in goods yard is abstracted into the point in solution space, represents the potential solution of problem, and the quality of goods yard i corresponds to the adaptation of solution
Angle value fiti, quantity of the NP for goods yard, i=1,2, NP；
Cargo is divided by ABC algorithm to be led bee, follows 3 seed type of bee and search bee, wherein leading bee and bee being followed respectively to account for cargo
The half of total amount, quantity is equal to the quantity in goods yard, and there was only one in the same time of every goods yard and lead bee on goods yard；
Assuming that the dimension of Solve problems is D, in t iteration, the position of goods yard i is expressed as x 'i={ x 'i1,x′i2,···x
′iD, wherein t indicates current the number of iterations；xid∈[Ld,Ud],LdAnd UdRespectively indicate the lower and upper limit of search space, d
=1,2, D；
The initial position of goods yard i is randomly generated in all goods yards according to described above；
xid=Ld+rand(0,1)(Ud-Ld)； (8)
In the search incipient stage；
It leads bee to be searched for around the L of goods yard according to formula and generates a new goods yard；
In formula: d is a random integers in [1, D], and expression leads bee to be randomly chosen one-dimensional scan for；j∈{1,
2, NP }, indicate to randomly choose the goods yard for being not equal to i in NP goods yard；It is that [- 1,1] are equally distributed random
Number determines perturbation amplitude；
As new goods yard Vi=[vi1,vi2,···vid] fitness be better than XiWhen, using the method V of greediness selectioniInstead of Xi,
Otherwise retain Xi；
After all operations for leading bee perfect (9), goods yard information is shared in the information interchange area that flies back；
It follows bee according to the goods yard information for leading bee to share, is followed by the probability that formula (10) calculate；
It follows bee to lead bee using the method choice of roulette, i.e., an equally distributed random number r is generated in [0,1], if pi
Greater than r, this follows bee to generate a new goods yard around the i of goods yard, and using with the method for leading the identical greedy selection of bee
Determine the goods yard retained；
The stage in search process；
If goods yard XiBy trial iterative search arrival threshold value limit without finding better goods yard, goods yard XiIt will
It can be abandoned, it is corresponding to lead bee diversification in role for search bee；
A new goods yard will be randomly generated instead of X in search space in search beei, the above process such as formula (11)；
In ABC algorithm, the fitness evaluation of solution is calculated according to formula (12)；
5. the die storehouse Intelligent partition storage method according to claim 4 based on artificial bee colony algorithm, it is characterised in that:
Start artificial bee colony algorithm process, is divided into following steps progress；
Step 1: each goods yard X is initializedi；Setup parameter NP, limit and maximum number of iterations；T=1；
Step 2: for goods yard XiDistribution one leads bee, scans for by formula (9), generates new goods yard Vi；
Step 3: V is evaluated according to formula (12)iFitness, according to greediness selection method determine retain goods yard；
Step 4: the probability that the goods yard for leading bee to find is followed is calculated by formula (10)；
Step 5: following bee to scan for using mode identical with bee is led, and determines reservation according to the method for greediness selection
Goods yard；
Step 6: judge goods yard XiWhether the condition that satisfaction is abandoned, if it is satisfied, lead bee be converted into investigation bee, otherwise directly
Go to step 8；
Step 7: new goods yard is randomly generated according to formula (11) in investigation bee；
Step 8: t=t+1 judges whether to meet termination condition, if so, output most solves, otherwise goes to step two, solved
Journey terminates；
Finally, come out the objective function of foundation with graphical representation in conjunction with MATLAB into the implementation phase of artificial bee colony algorithm,
Realize the visualization of calculated result and programming.
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| CN111674795A (en) * | 2020-05-27 | 2020-09-18 | 浙江工业大学 | Task scheduling method of cross-layer and cross-roadway shuttle storage system |
| CN112100861A (en) * | 2020-09-22 | 2020-12-18 | 河南中烟工业有限责任公司 | Cigarette production material goods space distribution method based on invasive weed optimization algorithm |
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| CN112633729A (en) * | 2020-12-29 | 2021-04-09 | 杭州电子科技大学 | Multi-compartment material vehicle cargo space optimization method based on human factors and Epsilon greedy algorithm |
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| CN114781269A (en) * | 2022-05-09 | 2022-07-22 | 江苏佳利达国际物流股份有限公司 | Three-dimensional warehouse optimization method based on cat swarm algorithm and three-dimensional warehouse |
| CN114936942A (en) * | 2022-07-21 | 2022-08-23 | 深圳市绽放工场科技有限公司 | Computer network data processing and analyzing system and method for insurance user |
| CN114936942B (en) * | 2022-07-21 | 2022-11-01 | 深圳市绽放工场科技有限公司 | Computer network data processing and analyzing system and method for insurance users |
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