CN110110841A - The method that multiple target honeybee breeding optimization algorithm solves the problems, such as flexible technology planning green manufacturing - Google Patents
The method that multiple target honeybee breeding optimization algorithm solves the problems, such as flexible technology planning green manufacturing Download PDFInfo
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Abstract
The present invention relates to the method that multiple target honeybee breeding optimization algorithm solves the problems, such as flexible technology planning green manufacturing, the existing honeybee of effective solution mates the deficiency that optimization algorithm is applied in green flexible technology planning problem；Main improvement content is as follows: since the solution of multi-objective optimization question is one group of solution rather than a solution, setting queen bee collection and saves the noninferior solution found in algorithm searching process；It needs to take into account multiple optimization aims according to green flexible technology planning problem, devises the drone based on Euclidean distance and queen mating method for calculating probability；The characteristics of for friendly process planning problem various dimensions flexible robot arms scheme, devises and cultivates young honeybee strategy based on the worker bee for becoming neighborhood search；Young honeybee differentiation strategy is devised based on quick non-dominated ranking strategy；The present invention improves basic honeybee mating optimization algorithm, proposes the multiple target honeybee mating optimization algorithm based on Pareto optimization theory, improves the computational efficiency for solving green flexible technology planning problem.
Description
Technical field
The present invention relates to process planning technical field, specifically multiple target honeybee breeding optimization algorithm solves flexible technology
The method for planning green manufacturing problem.
Background technique
The progress of economic rapid growth and human civilization, information technology, manufacturing technology and network technology obtain fast
Exhibition is hailed, science and technology brings more and more conveniences to the mankind, changes life, the production method of people, be greatly promoted people
Substance civilization and spiritual civilization.Although science and technology is significant to human society, also allow simultaneously facing mankind it is increasingly tighter
High resource and environmental pressure, falls into the mankind among a series of predicament, such as global warming, environmental pollution, ecology danger
Machine etc., therefore sustainable development is the necessary selection of the mankind.Sustainable development necessarily be unable to do without the concern to using energy source, in
Developing country of the state as economic rapid growth, energy consumption is growing day by day, this adjoint pollution problem is brought to environment
Great pressure.Machining is the main production process of manufacturing industry, and lathe is energy consumption resource important in manufacturing process,
It is optimized for the energy consumption of machined part manufacturing process, carries out green manufacturing, to improving manufacturing sustainable development,
Realize that low-carbon manufacture is of great significance.
Process planning is to determine the process route of work pieces process in workshop, it is specified that workpiece from blank becomes the complete of part
Portion's process.When studying flexible technology planning problem, nearly excellent process route generally refers to processing single workpiece most
The minimum of big completion date, i.e. single goal Maximal Makespan minimize.It is not only wanted when to the optimization of flexible technology planning problem
The process choice and sequence for considering work pieces process feature will also be selected further and determine the processing machine under different process
Device and cutter, also suffering from precedence constraint between process at the same time influences, and constraint is also had between machining feature and is existed,
Such as it is first thick after after essence, the former head time, other etc. after first face metapore, first benchmark, therefore the problem is a typical NP-
Complete problem.In addition, not only needing to consider flexibility when studying the flexible technology planning problem under green manufacturing mode
The economic benefits indicator of process planning link also needs to consider green index, carbon emission caused by energy consumption when such as work pieces process,
In comprising caused by carbon emission, lathe one-shot, preheating and the energy consumption of stopping caused by every procedure machine tooling energy consumption
When carbon emission caused by carbon emission, coolant consumption, carbon emission caused by lubrication oil consumption, process sequence processing transformation lathe
Carbon emission caused by haulage equipment energy consumption considers the problems of that green flexible technology planning meets the sustainable manufacturing that country advocates
Theory.But due to carrying out process planning while needing to take into account economic indicator and green index, with conventional flex Process Planning
The problem of drawing is compared, and green flexible technology planning problem is more complicated, and it is extremely important to design efficient method for solving.
Honeybee mating optimization (Honey Bees Mating Optimization, abbreviation HBMO) algorithm is a kind of imitation
The Swarm Intelligent Algorithm of honeybee reproductive behavior is taught by the Abass of University of New South Wales and was mentioned in 2001
Out.HBMO algorithm starts from a complete bee colony, and bee colony is made of three kinds of queen bee, drone and worker bee honeybees, the honey of three types
Effect of the bee in algorithm searching process is different.The optimal honeybee of fitness value carries out nuptial flight as queen bee in bee colony,
Marriage is carried out with certain probability and drone, generates young honeybee, worker bee is responsible for cultivating young honeybee, according to young honeybee fitness value
Difference, be further divided into new queen bee and drone.Traditional honeybee mating optimization algorithm answering in combinatorial optimization problem
It is preferable with effect, the case where being generally better than genetic algorithm and particle swarm optimization algorithm, but be only applicable to single object optimization.
Green multi-objective flexible process planning problem (Green Multi-Objective Flexible Process
Planning, GMOFPP) it is exactly the process route that workpiece is determined there are the constraint of multiple and different machining features, it should
Process route need to take into account efficiency index (Maximal Makespan) and low-carbon index (total carbon emissions amount), skill through the invention
Art scheme can be sought to one group of equilibrium solution.Following two target need to be combined for GMOFPP problem: minimizing maximum complete working hour
Between and minimize total carbon emissions amount.
The present invention will change basic honeybee mating optimization algorithm to solve green flexible technology planning problem
Into economic indicator and green index, propose the multiple target honeybee mating optimization based on Pareto optimization theory and calculate in order to balance
Method improves the computational efficiency for solving green flexible technology planning problem.
Summary of the invention
For above situation, to overcome the shortcomings of existing technologies, the present invention provides a kind of multiple target honeybee breeding optimization calculation
The method that method solves the problems, such as flexible technology planning green manufacturing, this method can solve existing honeybee mating optimization algorithm green
The deficiency of color flexible technology planning problem application solves traditional honeybee mating optimization algorithm and is difficult to optimize green flexibility simultaneously
The problem of process planning problem multiple optimization aims.Main improvement content is as follows: since the solution of multi-objective optimization question is one group
Solution rather than a solution, therefore set queen bee collection and save the noninferior solution that finds in algorithm searching process；According to green flexible technology
Planning problem needs to take into account multiple optimization aims, devises the drone based on Euclidean distance and queen mating method for calculating probability；
The characteristics of for friendly process planning problem various dimensions flexible robot arms scheme, is devised and is cultivated based on the worker bee for becoming neighborhood search
Young honeybee strategy；Young honeybee differentiation strategy is devised based on quick non-dominated ranking strategy.
The present invention includes the next steps:
Step 1: parameter setting, setting multiple target honeybee mating optimization algorithm solve green flexible technology planning problem
Relevant parameter, comprising: bee colony size PopSize, young honeybee number BroodSize, worker bee number WorkerNum, queen bee collect scale
QueenSize, queen bee spermatheca size SperNum, energy threshold T when queen bee flight, energy and speed when queen bee flight
Attenuation coefficient α, worker bee cultivate the number of iterations IterMax, algorithm iteration number GenMax of young honeybee；
Step 2: the honeybee individual in initialization bee colony, each honeybee represent a feasible flexible technology programme,
The initialization of queen bee collection and drone, current iteration number are Gen=0；
Step 3: concentrating one queen bee of random selection in queen bee, carry out nuptial flight, and generate young honeybee population；
Step 4: worker bee cultivates young honeybee, each worker bee is equivalent to a kind of local searching strategy；
Step 5: the update of queen bee collection and male peak population；Male peak population and young honeybee population are merged into a new population
Ptemp；Quick non-dominated ranking is carried out to the individual of new population, constructs non-dominant grade curved surface { F1, F2..., Fn}；According to etc.
Grade or crowding are updated queen bee collection and male peak population；
Step 6:Gen=Gen+1 judges whether to meet algorithm stop criterion, i.e. whether algorithm iteration number reaches maximum
The number of iterations GenmaxIf Gen < Genmax, then step 3 is jumped to；Otherwise, the queen bee collection finally obtained, algorithm knot are exported
Beam.
Preferably, 0 < α < 1 of attenuation coefficient.
Preferably, the termination condition of algorithm are as follows: queen bee collection number reaches QueenSize and Gen < Genmax, algorithm end
Only；Otherwise Gen is run tomaxGeneration, termination algorithm.
Preferably, between bee colony number P opSize value 100-400；
Young honeybee number BroodSize and bee colony number P opSize are consistent；
Worker bee number WorkerSize is customized；
Queen bee collection number QueenSize generally takes the 10%-20% of bee colony number P opSize；
Queen bee spermatheca SperSize takes the 50%-60% of bee colony number P opSize；
Threshold value threshold takes 0.1%-0.4% when queen bee flight；
The number of iterations L of worker bee cultivation young honeybeemaxThe number of iterations Gen is terminated with algorithmmaxUnanimously, between value 50-200.
Its application field is extended to flexible technology rule by improvement by traditional honeybee breeding optimization algorithm by the present invention
It draws in green manufacturing problem, multiple targets can be optimized simultaneously with traditional HBMO algorithm ratio in multiple target HBMO algorithm；Setting
Queen bee collection, queen bee collection are equivalent to a kind of external archive maintenance strategy.It can both save the noninferior solution of parent population, simultaneously
Also the generation of population young honeybee can be participated in；Using quick non-dominated ranking method as in multiple target HBMO algorithm queen bee collection and
The more new strategy of drone population promotes the optimizing of algorithm to search for.
Detailed description of the invention
Fig. 1 is inventive algorithm flow chart.
Fig. 2 is problem-instance figure of the present invention.
Fig. 3 is that young honeybee of the present invention generates figure one.
Fig. 4 is that young honeybee of the present invention generates figure two.
Fig. 5 is that worker bee feature of the present invention cultivates figure.
Fig. 6 is that worker bee technique of the present invention cultivates figure.
Fig. 7 is the distribution map of Pareto disaggregation of the present invention.
Specific embodiment
For the present invention aforementioned and other technology contents, feature and effect refer to attached drawing 1 to Fig. 7 pairs in following cooperation
In the detailed description of embodiment, can clearly it present.The structure content being previously mentioned in following embodiment is with specification
Attached drawing is reference.
Each exemplary embodiment of the invention is described below with reference to accompanying drawings.
Step 1: parameter setting, setting multiple target HBMO algorithm solve the correlation of flexible technology planning green manufacturing problem
Parameter, comprising: bee colony number P opSize, young honeybee number BroodSize, worker bee number WorkerSize, queen bee collect number
QueenSize, queen bee spermatheca capacity SperSize, threshold value threshold when queen bee flight, queen bee energy and speed decline
Subtract factor alpha, worker bee cultivates the number of iterations L of young honeybeemax。
Wherein 0 < α < 1 of attenuation coefficient, generally takes 0.7~0.9 or so, α that should not take too small, not so spermatheca is not easy
It fills up.
Male peak population is that bee colony number P opSize subtracts queen bee collection number QueenSize, enables DroneSize table at this time
It is shown as drone population.
The termination condition of multiple target HBMO algorithm are as follows: queen bee collection number reaches QueenSize and Gen < Genmax, algorithm
It terminates；Otherwise Gen is run tomaxGeneration, termination algorithm.
Explanation about variable-value: bee colony number P opSize is customized, needs the processing energy with computer according to problem
Power is determining, can be between value 100-400.
Young honeybee number BroodSize and bee colony number P opSize are consistent.
Worker bee number WorkerSize is customized, and each worker bee is equivalent to a kind of local searching strategy.It should be noted that
If worker bee cultivates the feature string of young honeybee, the constraint that the feature string after cultivation is possible to be unsatisfactory for feature is closed
System needs that infeasible solution is converted to feasible solution using constraint processing method at this time.In the case where defining multiple workers bee,
It needs to select worker bee using roulette method.
Queen bee collection number QueenSize generally takes the 10%-20% of bee colony number, so that it may compare the good of solution
It is bad.
Queen bee spermatheca SperSize takes the 50%-60% of bee colony number.
Threshold value threshold should not take too big when queen bee flight, generally take between 0.1%-0.4%.Queen bee energy
With speed random value.
The number of iterations L of worker bee cultivation young honeybeemaxThe number of iterations Gen is terminated with algorithmmaxUnanimously, between value 50-200,
General value 100.
Step 2: random initializtion bee colony；Empty queen bee collection, the initialization of queen bee collection and drone；Enable Gen=0.
The initialization of queen bee collection and drone: non-dominated ranking first is carried out to current bee colony；If current population is non-dominant
The number of solution is greater than the number of queen bee collection, then the crowding distance for non-domination solution being concentrated all individuals is needed to calculate, will be crowded
Queen bee collection is put into apart from big individual；If the number of the non-domination solution of current population is less than the number of queen bee collection, will own
Non-domination solution be directly introduced to queen bee concentration；All individuals that queen bee is concentrated, bee colony removal are removed from bee colony PopSize
PopSize afterwards is defined as male peak population.
An example, variable-value in the example are given in Fig. 2 are as follows: bee colony number is 100,100 queen bee of young honeybee number
Collecting number is 10, and queen bee spermatheca is 60, and worker bee number is 2, and energy threshold when queen bee flight is 0.001, queen bee speed and
The attenuation coefficient of energy is 0.9, and worker bee cultivates the number of iterations 100 of young honeybee, algorithm iteration number 100.Wherein objective function two
It is a, optimize while to carbon emission amount function and the function of time.
Step 3: the queen bee that random selection queen bee is concentrated carries out nuptial flight, and generate young honeybee population.
The queen bee nuptial flight stage
It is concentrated at random from queen bee and selects a queen bee, empty the spermatheca of queen bee；The speed (Speed) of random queen bee and
Energy (Energy)；If a is to enable a=0 with the successful drone number of queen bee nuptial flight；If t is the number of flights of queen bee, t=is enabled
0；It performs the following operation, as shown in Fig. 1 algorithm flow chart.
A) drone is randomly choosed from drone population PopSize.
B) mate probability value of the drone with queen bee is calculated, probability value formula:What D was represented
Drone, Q represent queen bee；Speed (t) is flying speed of the queen bee in the t times flight；Δ (f) is drone and queen bee fitness
The absolute value of difference because can not determine the specific fitness value of every honeybee in multiple target HBMO algorithm, therefore takes WithI-th of target function value of queen bee and drone is respectively represented, wherein
I ∈ { 1,2 }.
C) define random number between 0-1, to judge drone whether with queen mating, if drone and queen bee mate
Probability value is greater than the random number, then carries out marriage in the spermatheca of the genotype deposit queen bee of drone, enable a=a+1 and in hero
The drone is deleted in bee colony PopSize；Otherwise, the update of queen bee speed and energy is directly carried out without marriage.
D) update of queen bee speed and energy；
By formula: the speed of Speed (t+1)=α × Speed (t), Energy (t+1)=α × Energy (t) to queen bee
It is updated with energy, enables t=t+1 after update.
If the drone that the ENERGY E nergy (t) of queen bee is greater than energy threshold threshold and is stored in spermatheca at this time
When number is less than, i.e. Energy (t) > threshold and a < SperSize jump to a) step repetitive cycling.When queen bee energy
When whether amount has expired less than threshold value or queen bee spermatheca, nuptial flight terminates, and carries out operation below.
Young honeybee generation phase
The genotype that a drone is randomly choosed in the spermatheca of queen bee, to queen bee and the genotype use as Fig. 3,
Crossover operation shown in Fig. 4 generates young honeybee, until generating BroodSize young honeybee.Every honeybee all includes three sequences
Column, are optional feature sequence, optional process sequence, optional machine sequencing respectively.Process sequence, machine in these three sequences
Sequence crossover mode is identical, therefore is next only described in detail as follows to characteristic sequence and the specific crossover operation of technique shown.
Numerical value on Fig. 3 indicates the specific feature machining sequence of workpiece, for example the 5-4-2-3-1-6-7 of queen bee is represented
It is the processing sequence of feature 5- feature 4- feature 2- feature 3- feature 1- feature 6- feature 7；What the numerical value on Fig. 4 indicated is work
The specific processing technology number of part, for example, queen bee 1-1-1-1-2-1-1 represent be feature 5 select No. 2 processing technologys, feature 4
No. 1 processing technology, feature 2 is selected to select No. 1 processing technology, feature 3 that No. 1 processing technology, feature 1 is selected to select No. 2 processing works
Skill, feature 1 select No. 1 processing technology, feature 1 to select No. 1 processing technology.
The crossover operation of characteristic sequence: on the characteristic sequence of queen bee and drone, random two crosspoints, such as Fig. 3 institute
Show, by intermediate features sequence 2-3-1,1-3-5 in two crosspoints of queen bee and drone, copies to young honeybee 1 and young honeybee 2 respectively
Intermediate sequence.
Delete the existing character numerical value of young honeybee 2 in queen bee；Delete the existing character numerical value of young honeybee 1 in drone；It is empty in Fig. 3
Line indicates the numerical value deleted.
By the remaining character numerical value of queen bee according to successively filling in young honeybee 2 remaining vacancy from a left side to sequence again；Similarly will
The remaining character numerical value of drone successively fills remaining vacancy in young honeybee 1.
The crossover operation of optional process sequence: on the process sequence of queen bee and drone, random two crosspoints, such as Fig. 4
It is shown, process sequence is divided into three parts, is divided into stem, middle part, tail portion.1 previous section of crosspoint is stem, and crosspoint 1 is arrived
It is middle part between crosspoint 2,2 aft section of crosspoint is tail portion.
Technique intersection, i.e. two-point crossover are carried out as shown in Figure 4.The middle part of queen bee and drone copies to young honeybee 1 and children respectively
The middle part of bee 2, the stem of queen bee, tail portion copy to the stem of young honeybee 2, tail portion, and the stem of drone, tail portion copy to young honeybee 1
Stem, tail portion.
Step 4: worker bee cultivates the young honeybee stage.When often generating a young honeybee by crossover operator, all need to be equipped with a worker bee
It is cultivated.The characteristics of for friendly process planning problem various dimensions flexible robot arms scheme, devises based on change neighborhood search
Worker bee cultivates young honeybee strategy, and in multiple target HBMO algorithm, each worker bee is all equivalent to a kind of local searching strategy, worker bee
Cultivate the process that process, that is, local searching strategy of young honeybee updates young honeybee.Fig. 2 example has used two when carrying out proof of algorithm
The different field structure of kind (uses N1、N2To indicate) constitute worker bee, worker bee number 2, what t was represented is that current worker bee cultivates children
The algebra of bee.
N1: a position on random selection young honeybee characteristic sequence judges to can be inserted into the case where meeting feature constraint
Numerical value radom insertion in this position is met any other position under the constraint relationship, such as Fig. 5 by the other positions of the sequence
It is shown.
N2: a position of optional process sequence in young honeybee, optional processing machine sequence is randomly choosed, according to technique, is added
The optional situation of work machine determines position using roulette method if optional operation resource only has one kind at random again, no
Then, another operation resource is selected to replace current operation resource, as shown in Figure 6.
Enable t=1；
A) a worker bee N is randomly choosed using roulette methodiCurrent young honeybee j is cultivated, obtain new young honeybee j' its
Middle i ∈ { 1,2 }；
B) judge whether new young honeybee j ' can dominate young honeybee j, if it is then young honeybee j replaces young honeybee j；Otherwise it abandons
New young honeybee j ' carries out subsequent operation to young honeybee j.
C) t=t+1 is enabled；If t < Lmax, jump to a) step and carry out repetitive cycling.Otherwise, cultivating process is terminated.
Step 5: after all young honeybees generate, queen bee collection and male peak population being updated.
Male peak population and young honeybee population are merged into a new population Ptemp；Quickly non-branch is carried out to the individual of new population
With sequence, non-dominant grade curved surface { F is constructed1,F2,...,Fn}。
The update of queen bee collection: by the highest F of non-dominant grade curved surface1The individual of grade and the individual of current queen bee collection close
And be a new population, find out all non-domination solutions of new population point.
Judge whether the number of the non-domination solution is greater than QueenSize.
If whether the number of the non-domination solution is greater than QueenSize, gathering around for the individual of these non-domination solutions is calculated
It squeezes distance to be arranged successively the crowding distance of each individual from big to small, QueenSize individual is deposited into queen bee collection before taking
In.
If whether the number of the non-domination solution is less than or equal to QueenSize, all non-domination solutions are deposited into
Queen bee is concentrated；It is stored into the individual from F of queen bee collection1In delete；Update new population PtempIndividual amount.
The update of drone population: drone population is emptied；By grade successively by F1,F2... in individual be deposited into drone kind
In group；When being stored to FiWhen rank, the individual amount of deposit drone group is greater than DroneSize, i ∈ { 1,2,3 ..., n }, at this time
Calculate FiThe crowding of the every individual of grade；It by the crowding distance of each individual, is arranged successively from big to small, takes preceding DroneSize
Individual is deposited into drone concentration.
Gen=Gen+1；Judge whether to meet algorithm stop criterion, i.e. whether algorithm iteration number reaches greatest iteration time
Number Genmax；If Gen < Genmax, then step 3 is jumped to, as shown in algorithm flow Fig. 1；Otherwise, the bee finally obtained is exported
Wang Ji, algorithm terminate.
The problem of Fig. 2, is solved, with the minimum objective function of Maximal Makespan minimum and maximum carbon emission amount,
It is calculated by the solution that traditional honeybee breeds optimization algorithm, when single goal time optimal 522, single goal carbon emission amount is optimal
When 2349.68.It is calculated when with the solution of improved multiple target HBMO algorithm, there are multiple solutions, organizing that solution concentrates at this still can be with
Solution optimal when any single object optimization is found, the non-dominant disaggregation furthermore acquired not only can objectively reflect GMOFPP
Relationship between the multiple target values of problem to provide a variety of alternatives for policymaker, and can reduce phase between target
Actual production is more instructed in influence of the factor and policymaker's subjective uncertainty factor mutually restricted for problem solving
Meaning.For examples detailed above, the distribution map of the Pareto disaggregation acquired using technical method of the invention is as shown in Figure 7.
The invention proposes a kind of multiple target honeybee breeding optimization algorithms to solve the problems, such as flexible technology planning green manufacturing
Method is for setting up a queen bee collection in algorithm, is equivalent to a kind of external archive maintenance strategy, it can both save parent population
Noninferior solution, while can also participate in the generation of population young honeybee to save defect individual；Using non-dominated ranking, queen bee is collected
It is initialized with drone population, using quick non-dominated ranking method as queen bee collection and drone in multiple target HBMO algorithm
The more new strategy of population devises and cultivates young honeybee strategy based on the worker bee for becoming neighborhood search, the optimizing of algorithm is promoted to search for.Most
The multiple target HBMO algorithm for proposing improvement is verified by example afterwards, calculated result proves that the method proposed is effective.
Claims (4)
1. the method that multiple target honeybee breeding optimization algorithm solves the problems, such as flexible technology planning green manufacturing, which is characterized in that packet
Include following steps:
Step 1: parameter setting, setting multiple target honeybee mating optimization algorithm solve the related ginseng of green flexible technology planning problem
Number, comprising: bee colony size PopSize, young honeybee number BroodSize, worker bee number WorkerNum, queen bee collect scale
QueenSize, queen bee spermatheca size SperNum, energy threshold T when queen bee flight, energy and speed when queen bee flight
Attenuation coefficient α, worker bee cultivate the number of iterations IterMax, algorithm iteration number GenMax of young honeybee；
Step 2: the honeybee individual in initialization bee colony, each honeybee represent a feasible flexible technology programme, queen bee
The initialization of collection and drone, current iteration number are Gen=0；
Step 3: concentrating one queen bee of random selection in queen bee, carry out nuptial flight, and generate young honeybee population；
Step 4: worker bee cultivates young honeybee, each worker bee is equivalent to a kind of local searching strategy；
Step 5: the update of queen bee collection and male peak population；Male peak population and young honeybee population are merged into a new population Ptemp；To new
The individual of population carries out quick non-dominated ranking, constructs non-dominant grade curved surface { F1, F2..., Fn}；According to grade or crowding
Queen bee collection and male peak population are updated；
Step 6:Gen=Gen+1 judges whether to meet algorithm stop criterion, i.e. whether algorithm iteration number reaches greatest iteration
Number GenmaxIf Gen < Genmax, then step 3 is jumped to；Otherwise, the queen bee collection finally obtained is exported, algorithm terminates.
2. multiple target honeybee breeding optimization algorithm according to claim 1 solves the problems, such as flexible technology planning green manufacturing
Method, which is characterized in that 0 < α < 1 of attenuation coefficient.
3. multiple target honeybee breeding optimization algorithm according to claim 2 solves the problems, such as flexible technology planning green manufacturing
Method, which is characterized in that the termination condition of multiple target HBMO algorithm are as follows: queen bee collection number reaches QueenSize and Gen <
Genmax, algorithm termination；Otherwise Gen is run tomaxGeneration, termination algorithm.
4. multiple target honeybee breeding optimization algorithm according to claim 3 solves the problems, such as flexible technology planning green manufacturing
Method, which is characterized in that the value range of bee colony number P opSize is between 100-400, bee colony number P opSize value
Between 100-400；
Young honeybee number BroodSize and bee colony number P opSize are consistent；
Worker bee number WorkerSize is customized；
Queen bee collection number QueenSize generally takes the 10%-20% of bee colony number P opSize；
Queen bee spermatheca SperSize takes the 50%-60% of bee colony number P opSize；
Threshold value threshold takes 0.1%-0.4% when queen bee flight；
The number of iterations L of worker bee cultivation young honeybeemaxThe number of iterations Gen is terminated with algorithmmaxUnanimously, between value 50-200.
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