CN105976048A - Power transmission network extension planning method based on improved artificial bee colony algorithm - Google Patents

Power transmission network extension planning method based on improved artificial bee colony algorithm Download PDF

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CN105976048A
CN105976048A CN201610274322.4A CN201610274322A CN105976048A CN 105976048 A CN105976048 A CN 105976048A CN 201610274322 A CN201610274322 A CN 201610274322A CN 105976048 A CN105976048 A CN 105976048A
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nectar source
represent
fitness
source
transmission network
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岳东
高�浩
夏星宇
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Suzhou Fanneng Electric Power Technology Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
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    • G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention provides a power transmission network extension planning method based on an improved artificial bee colony algorithm. Based on a local search method which carries out intersection operation with a globally optimal solution, a better honey source is searched to substitute an original honey source to be served as an optimal honey source. Under the condition that searching optimization is guaranteed, through carrying out two-item intersection on a current solution and the globally optimal solution, an exploitation capability of an algorithm is enhanced so that the method possesses a good global search and local exploitation capability; high searching optimization precision and a faster convergence speed in an application of power transmission network extension planning are possessed.

Description

A kind of based on the method for expansion planning of power transmission network improving artificial bee colony algorithm
Technical field
The invention belongs to Power System Planning field, a kind of based on the Transmission Expansion improving artificial bee colony algorithm Planing method.
Background technology
Along with modern urbanization progress faster, urban power consumption increases severely, brings immense pressure to power plant and power transmission network. At electric power resource by unified state control, in the case of electric power networks is built by unification of the motherland, consider that each is non-the most flexibly The plant-grid connection power system of power system, optimizes the extension transformation of power transmission network, becomes current China electric power enterprise and is badly in need of solving Problem certainly.Electric power networks construction mainly includes power transformation.Three links of transmission and disttrbution, power transformation link mainly builds transformation Standing, power transmission network and distribution network planning are then the most important components of Electric Power Network Planning.The task of Transmission network expansion planning is one Fixed project period internal loading growth pattern and power source planning scheme known on the premise of, determine corresponding optimal electric network composition, The expenses such as construction, operation are made every effort to minimum while meeting reliability constraint.One rational Transmission network expansion planning scheme for The distributing rationally all to have of the safe operation of power system and electric power resource is of great significance.
Transmission network expansion planning problem is mathematically a large-scale mixed integer optimization problem, logical in prior art This technical problem is solved frequently with modern heuristic method.Modern heuristic method mainly includes that genetic algorithm, population are calculated Method, artificial bee colony algorithm and ant group algorithm, above-mentioned Swarm Intelligence Algorithm is due to its potential concurrency and distributivity feature, for place Manage the substantial amounts of data with database form existence provide technical support and obtained using widely.
Artificial bee colony algorithm is a kind of new Swarm Intelligent Algorithm, strong owing to having global convergence, convergence rate Hurry up, the advantage that robustness is good, artificial bee colony algorithm is widely used in the communications field, signal and image processing field, control neck Territory, field of power etc..Unlike other intelligent algorithms, artificial bee colony algorithm defines nectar source and three kinds of honeybees: adopt Apis, observation honeybee and investigation honeybee.Nectar source represents a solution vector, and its quality characterizes the fitness value of correspondence just, and gathering honey Honeybee and the effect observing honeybee are then the increased frequency making local optimal searching, wider, so that this algorithm has the strongest overall situation Search capability.But artificial bee colony algorithm there is also Premature Convergence, easily it is absorbed in local extremum in the later stage, the highest the lacking of search precision Point.
Summary of the invention
Technical problem solved by the invention is to provide a kind of based on the Transmission Expansion rule improving artificial bee colony algorithm The method of drawing, replaces green molasses source to make based on carrying out intersecting the more excellent nectar source of the local search approach searching of operation with globally optimal solution For optimum nectar source, by carrying out binomial with globally optimal solution intersect currently solving while ensureing optimizing so that it is possess higher Low optimization accuracy and faster convergence rate.
The technical solution realizing the object of the invention is:
A kind of method for expansion planning of power transmission network based on improvement artificial bee colony algorithm, comprises the following steps:
Step 1: with the minimum target of power transmission network construction cost, set up Optimized model in conjunction with constraints, and arrange artificial The parameter of bee colony；
Step 2: set up nectar source in a model and utilize unlearning method to initialize nectar source；
Step 3: calculate target function value and the fitness value in all nectar sources, using nectar source maximum for fitness value as optimum Nectar source；
Step 4: gathering honey honeybee carries out Local Search to nectar source, obtains iteration new explanation；
Step 5: calculate the fitness value of iteration new explanation, be updated nectar source according to Greedy principle, if green molasses source adapts to Angle value is the highest, then the number of times of exploitation in this nectar source increases by 1；
Step 6: observe honeybee and carry out local optimal searching, updates optimum nectar source according to roulette principle；
Step 7: determining whether that nectar source exceedes its maximum exploitation number of times, if having, then the Apis at nectar source is converted into gathering honey Honeybee also utilizes unlearning method again to find new nectar source, and carries out fitness value with green molasses source and compare, and updates optimum nectar source；If Nothing, forwards step 8 to；
Step 8: judge whether to reach maximum iteration time, the most then the current optimum nectar source of output is as optimal solution, and it is right The D dimensional vector answered is the preferred plan of Transmission network expansion planning；If it is not, forward step 4 to.
Further, the present invention based on improving the method for expansion planning of power transmission network of artificial bee colony algorithm, excellent in step 1 The object function changing model is:
F = Σ l ∈ Ω c l n l + W 1 ( Σ o l ( | f l | - f l ‾ ) ) + W 2 ( n l - n l ‾ )
Wherein, clRepresent every branch road stringing cost in the l article transmission of electricity corridor, nlRepresent the branch road in the l article transmission of electricity corridor Number, Ω represents the set in all transmission of electricity corridors, flRepresent total trend value of l branch road,Represent the trend threshold of l article of branch road Value, ol represents the branch road collection of all overloads,Represent that the maximum stringing in l article of corridor returns number, W1、W2For penalty coefficient.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, the people in step 1 The parameter of worker bee group includes: population scale Popsize, maximum iteration time Maxiter, nectar source maximum exploitation number of times Limit, friendship Fork probability CR, dimension D, the quantity of gathering honey honeybee and observation honeybee is all set to Popsize/2.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, initial in step 2 Concretely comprising the following steps of change nectar source:
Step 2-1: stochastic generation nectar source primitive solutionWherein, i represents i-th solution, and j represents J ties up component, and N represents nectar source number；
Step 2-2: calculate each primitive solution and the most reversely solve
Step 2-3: calculate primitive solution and the target function value reversely solved and fitness value；
Step 2-4: using solution maximum for fitness value as nectar source initial solution.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, fitting in step 3 The angle value is answered to be:
fitness i = 1 1 + f i , f i &GreaterEqual; 0 | f i | , f i < 0
Wherein, fiRepresent the target function value in i-th nectar source.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, local in step 4 Concretely comprising the following steps of search:
Step 4-1: arbitrarily choose two different nectar source Xi、XkJth dimension component, obtain target component vij；
Step 4-2: be calculated iteration new explanation:
Wherein, v 'ijRepresent the jth dimension component of the i-th target solution after variation, vijRepresent the jth dimension of i-th target solution Component,Representing the jth dimension component of current optimal solution, rand (-1,1) represents equally distributed random value between 0 to 1.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, in step 4-1 Target component vijFor:
vij=xij+rand()(xij-xkj)
Wherein, i, k ∈ 1,2 ..., N}, j ∈ 1,2 ..., D} randomly selects from set, but k can not be equal to I, rand () are value randoms number in [-1,1], xijRepresent nectar source XiJth dimension component, vkjRepresent nectar source XkJth dimension Component.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, basis in step 5 The nectar source that Greedy principle updates is:
x i + 1 = v i j , fitness v &GreaterEqual; fitness x x i j , fitness v < fitness x
Wherein, xijRepresent nectar source XiJth dimension component, vijRepresent the jth dimension component of i-th target solution, fitnessxTable Show xijFitness value, fitnessvRepresent vijFitness value.
Further, the method for expansion planning of power transmission network based on improvement artificial bee colony algorithm of the present invention, local in step 6 Concretely comprising the following steps of optimizing:
Step 6-1: observe honeybee according to the fitness value calculation in nectar source and probability is followed in i-th nectar sourceWherein, fitnessiFor fitness value；
Step 6-2: calculate the select probability in i-th nectar sourceWherein, i is the sequence number in nectar source, i ∈ [1,2, ... N], ii is the sequence number in sub-nectar source, ii ∈ [1,2 ... i], PiiRepresent the i-th i nectar source follows probability, P 'iFor front i honey Source follow probability cumulative and；
Step 6-3: equally distributed random number rand between generating 0 to 1, if rand is ＜ P 'i, then observe honeybee and select Go to the nectar source at i-th gathering honey honeybee place；If rand >=P 'i, then make i=i+1, and forward step 6-1 to, and judge that i is the biggest In nectar source sum N, the probability of following in the most all nectar sources was the most all judged, if i=N+1, then stops judging, continues step 6- 4；
Step 6-4: the nectar source observing honeybee place is carried out Local Search, obtains iteration new explanation, and update nectar source；If new explanation Be still green molasses source, then the number of times of exploitation in this nectar source increases by 1.The present invention uses above technical scheme compared with prior art, tool There is a techniques below effect:
1, the inventive method finds more excellent nectar source based on carrying out intersecting the Local Search of operation with globally optimal solution, strengthens The development ability of algorithm so that it is there is good global search and local development ability；
2, the method for the present invention possesses higher low optimization accuracy and faster convergence rate.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the present invention；
Fig. 2 is the primitive network scattergram of 6 node power transmission networks；
Fig. 3 is that the test result of difference algorithm, particle cluster algorithm, artificial bee colony algorithm and inventive algorithm compares；
Fig. 4 is the convergence curve comparison diagram of difference algorithm, particle cluster algorithm, artificial bee colony algorithm and inventive algorithm；
Fig. 5 is that the optimal of difference algorithm, particle cluster algorithm, artificial bee colony algorithm and inventive algorithm lays scheme ratio Relatively.
Detailed description of the invention
Embodiments of the present invention are described below in detail, and the example of described embodiment is shown in the drawings, the most ad initio Represent same or similar element to same or similar label eventually or there is the element of same or like function.Below by ginseng The embodiment examining accompanying drawing description is exemplary, is only used for explaining the present invention, and is not construed as limiting the claims.
It is illustrated in figure 1 the method for expansion planning of power transmission network flow chart based on improvement artificial bee colony algorithm of the present invention, tool Body comprises the following steps:
Step 1: with the minimum target of power transmission network construction cost, set up Optimized model in conjunction with constraints, and arrange artificial The parameter of bee colony.
Consider that one-time construction cost and constraints count object function with the form of penalty function, obtain Optimized model Object function is:
F = &Sigma; l &Element; &Omega; c l n l + W 1 ( &Sigma; o l ( | f l | - f l &OverBar; ) ) + W 2 ( n l - n l &OverBar; )
Wherein, clRepresent every branch road stringing cost in the l article transmission of electricity corridor, nlRepresent the branch road in the l article transmission of electricity corridor Number, Ω represents the set in all transmission of electricity corridors, flRepresent total trend value of l branch road,Represent the trend threshold of l article of branch road Value, ol represents the branch road collection of all overloads,Represent that the maximum stringing in l article of corridor returns number, W1、W2For penalty coefficient.
The parameter arranging artificial bee colony includes: population scale Popsize, maximum iteration time Maxiter, nectar source maximum are opened Adopting number of times Limit, crossover probability CR, dimension D, the quantity of gathering honey honeybee and observation honeybee is all set to Popsize/2.
The present embodiment is as a example by the power transmission network of 6 nodes, and its primitive network scattergram is as in figure 2 it is shown, relevant parameter is arranged such as Under: Popsize=40, Maxiter=100, Limit=100, CR=0.9, D=7.
This 6 node power grids circuits relevant parameter is as shown in the table:
Table 16 node power grids circuits relevant parameter
Step 2: set up nectar source in a model and utilize unlearning method to initialize nectar source, concretely comprising the following steps:
Step 2-1: stochastic generation nectar source primitive solutionWherein, i represents i-th solution, and j represents J ties up component, and N represents nectar source number.
Initialize population scale M, dimension D, the random initializtion stage:
For i=1:M Do
For j=1:D Do
xi,j=xmin,j+rand(0,1)×(xmax,j-xmin,j)
end For
end For
Wherein, xi,jRepresent primitive solution.
Step 2-2: calculate each primitive solution and the most reversely solve
For i=1:M Do
For j=1:D Do
oxi,j=xmin,j+xmax,j-xi,j
end For
end For
Wherein, oxi,jRepresent and reversely solve.
Step 2-3: calculate primitive solution and the target function value reversely solved and fitness value.
The computing formula of fitness value is:
fitness i = 1 1 + f i , f i &GreaterEqual; 0 | f i | , f i < 0
Wherein, fiRepresent the target function value in i-th nectar source.
Step 2-4: using solution maximum for fitness value as nectar source initial solution.
Step 3: calculate the target function value in all nectar sourcesAnd fitness valueUsing nectar source maximum for fitness value as optimum nectar source xGlobal。
Step 4: gathering honey honeybee carries out Local Search to nectar source, obtains iteration new explanation, concretely comprises the following steps:
Step 4-1: arbitrarily choose two different nectar source Xi、XkJth dimension component, obtain target component vij:
vij=xij+rand()(xij-xkj)
Wherein, i, k ∈ 1,2 ..., N}, j ∈ 1,2 ..., D} randomly selects from set, but k can not be equal to I, rand () are value randoms number in [-1,1], xijRepresent nectar source XiJth dimension component, vkjRepresent nectar source XkJth dimension Component；
Step 4-2: be calculated iteration new explanation:
Wherein, v 'ijRepresent the jth dimension component of the i-th target solution after variation, vijRepresent the jth dimension of i-th target solution Component,Representing the jth dimension component of current optimal solution, rand (-1,1) represents equally distributed random value between 0 to 1.
Step 5: calculate the fitness value of iteration new explanation, be updated nectar source according to Greedy principle, if green molasses source adapts to Angle value is the highest, then the number of times of exploitation in this nectar source increases by 1.
According to the nectar source that Greedy principle selection fitness is higher as new nectar source, the then nectar source updated it is:
x i + 1 = v i j , fitness v &GreaterEqual; fitness x x i j , fitness v < fitness x
Wherein, xijRepresent nectar source XiJth dimension component, vijRepresent the jth dimension component of i-th target solution, fitnessxTable Show xijFitness value, fitnessvRepresent vijFitness value.
Step 6: observe honeybee and carry out local optimal searching, updates the fitness value in optimum nectar source, i.e. nectar source according to roulette principle The highest, observe honeybee and go to the probability in this nectar source the biggest, concretely comprise the following steps:
Step 6-1: note nectar source number is N, observes honeybee according to the fitness value calculation in nectar source and follows i-th nectar source generally RateWherein, fitnessiFor fitness value；
Step 6-2: calculate the select probability in i-th nectar sourceWherein, i is the sequence number in nectar source, i ∈ [1,2, ... N], ii is the sequence number in sub-nectar source, ii ∈ [1,2 ... i], PiiRepresent the i-th i nectar source follows probability, P 'iFor front i honey Source follow probability cumulative and；
Step 6-3: equally distributed random value rand between generating 0 to 1, if rand is ＜ P 'i, then observe honeybee and select Go to the nectar source at i-th gathering honey honeybee place；Otherwise, make i=i+1, forward step 6-1 to, if i=N+1, stop；
Step 6-4: the nectar source observing honeybee place is carried out Local Search, obtains iteration new explanation, and updates optimum nectar source；If New explanation is still green molasses source, then the number of times of exploitation in this nectar source increases by 1, and updates optimum nectar source.
Step 7: determining whether that nectar source exceedes its maximum exploitation number of times Limit, if having, then the Apis at nectar source is converted into Gathering honey honeybee also utilizes unlearning method again to find new nectar source, and carries out fitness value with green molasses source and compare, and updates optimum nectar source； If nothing, forward step 8 to；
Step 8: judge whether to reach maximum iteration time Maxiter, the most then the current optimum nectar source of output is as optimum Solving, the D dimensional vector of its correspondence is the preferred plan of Transmission network expansion planning；If it is not, forward step 4 to.
Such as Fig. 3,4,5, the respectively test of difference algorithm, particle cluster algorithm, artificial bee colony algorithm and inventive algorithm Results contrast, convergence curve comparison diagram and most preferably lay project plan comparison.
The above is only the some embodiments of the present invention, it is noted that for the ordinary skill people of the art For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvement, these improve the guarantor that should be regarded as the present invention Protect scope.

Claims (9)

1. a method for expansion planning of power transmission network based on improvement artificial bee colony algorithm, it is characterised in that comprise the following steps:
Step 1: with the minimum target of power transmission network construction cost, set up Optimized model in conjunction with constraints, and artificial bee colony is set Parameter；
Step 2: set up nectar source in a model and utilize unlearning method to initialize nectar source；
Step 3: calculate target function value and the fitness value in all nectar sources, using nectar source maximum for fitness value as optimum nectar source xGlobal；
Step 4: gathering honey honeybee carries out Local Search to nectar source, obtains iteration new explanation；
Step 5: calculate the fitness value of iteration new explanation, according to Greedy principle, nectar source is updated, if green molasses source fitness value The highest, then the number of times of exploitation in this nectar source increases by 1；
Step 6: observe honeybee and carry out local optimal searching, updates optimum nectar source according to roulette principle；
Step 7: determining whether that nectar source exceedes its maximum exploitation number of times, if having, then the Apis at nectar source is converted into gathering honey honeybee also Utilize unlearning method again to find new nectar source, and carry out fitness value with green molasses source and compare, update optimum nectar source；If nothing, turn To step 8；
Step 8: judge whether to reach maximum iteration time, the most then the current optimum nectar source of output is as optimal solution, its correspondence D dimensional vector is the preferred plan of Transmission network expansion planning；If it is not, forward step 4 to.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that The object function of the Optimized model in step 1 is:
F = &Sigma; l &Element; &Omega; c l n l + W 1 ( &Sigma; o l ( | f l | - f l &OverBar; ) ) + W 2 ( n l - n l &OverBar; )
Wherein, clRepresent every branch road stringing cost in the l article transmission of electricity corridor, nlRepresent the circuitry number in the l article transmission of electricity corridor, Ω Represent the set in all transmission of electricity corridors, flRepresent total trend value of l branch road,Represent the trend threshold value of l article of branch road, ol table Show the branch road collection of all overloads,Represent that the maximum stringing in l article of corridor returns number, W1、W2For penalty coefficient.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that The parameter of the artificial bee colony in step 1 includes: population scale Popsize, maximum iteration time Maxiter, nectar source maximum are exploited Number of times Limit, crossover probability CR, dimension D, the quantity of gathering honey honeybee and observation honeybee is all set to Popsize/2.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that Step 2 initializes the concretely comprising the following steps of nectar source:
Step 2-1: stochastic generation nectar source primitive solutionWherein, i represents i-th solution, and j represents jth dimension point Amount, N represents nectar source number；
Step 2-2: calculate each primitive solution and the most reversely solve
Step 2-3: calculate primitive solution and the target function value reversely solved and fitness value；
Step 2-4: using solution maximum for fitness value as nectar source initial solution.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that Fitness value in step 3 is:
fitness i = 1 1 + f i , f i &GreaterEqual; 0 | f i | , f i < 0
Wherein, fiRepresent the target function value in i-th nectar source.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that The concretely comprising the following steps of Local Search in step 4:
Step 4-1: arbitrarily choose two different nectar source Xi、XkJth dimension component, obtain target component vij；
Step 4-2: be calculated iteration new explanation:
Wherein, v 'ijRepresent the jth dimension component of the i-th target solution after variation, vijRepresent the jth dimension component of i-th target solution,Representing the jth dimension component of current optimal solution, rand (-1,1) represents equally distributed random value between 0 to 1.
It is the most according to claim 6 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that Target component v in step 4-1ijFor:
vij=xij+rand()(xij-xkj)
Wherein, i, k ∈ 1,2 ..., N}, j ∈ 1,2 ..., D} randomly selects from set, but k can not be equal to i, Rand () is value random number in [-1,1], xijRepresent nectar source XiJth dimension component, vkjRepresent nectar source XkJth dimension point Amount.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that The nectar source updated according to Greedy principle in step 5 is:
x i + 1 = v i j , fitness v &GreaterEqual; fitness x x i j , fitness v < fitness x
Wherein, xijRepresent nectar source XiJth dimension component, vijRepresent the jth dimension component of i-th target solution, fitnessxRepresent xij Fitness value, fitnessvRepresent vijFitness value.
It is the most according to claim 1 based on the method for expansion planning of power transmission network improving artificial bee colony algorithm, it is characterised in that The concretely comprising the following steps of local optimal searching in step 6:
Step 6-1: observe honeybee according to the fitness value calculation in nectar source and probability is followed in i-th nectar sourceIts In, fitnessiFor fitness value；
Step 6-2: calculate the select probability in i-th nectar sourceWherein, i is the sequence number in nectar source, i ∈ [1,2 ... N], Ii is the sequence number in sub-nectar source, and ii ∈ [1,2 ... i], PiiRepresent the i-th i nectar source follows probability, P 'iFor front i nectar source with Cumulative with probability and；
Step 6-3: equally distributed random number rand between generating 0 to 1, if rand is ＜ P 'i, then observe honeybee and select to go to The nectar source at i-th gathering honey honeybee place；If rand >=P 'i, then make i=i+1, and forward step 6-1 to, and judge that whether i is more than honey Source sum N, the probability of following in the most all nectar sources was the most all judged, if i=N+1, then stops judging, continues step 6-4；
Step 6-4: the nectar source observing honeybee place is carried out Local Search, obtains iteration new explanation, and update nectar source；If new explanation is still Green molasses source, then the number of times of exploitation in this nectar source increases by 1.
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CN111291855A (en) * 2020-01-19 2020-06-16 西安石油大学 Natural gas circular pipe network layout optimization method based on improved intelligent algorithm
CN113537439A (en) * 2020-04-17 2021-10-22 中国石油化工股份有限公司 Improved artificial bee colony optimization algorithm
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CN107451692A (en) * 2017-08-02 2017-12-08 中国航空工业集团公司西安飞机设计研究所 A kind of aviation Spares method for optimizing configuration based on artificial bee colony algorithm
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CN108513234A (en) * 2018-03-14 2018-09-07 南京邮电大学 Based on the loud speaker volume optimization method for improving artificial bee colony algorithm
CN108513234B (en) * 2018-03-14 2020-03-20 南京邮电大学 Loudspeaker volume optimization method based on improved artificial bee colony algorithm
CN108596118B (en) * 2018-04-28 2021-04-23 北京师范大学 Remote sensing image classification method and system based on artificial bee colony algorithm
CN108596118A (en) * 2018-04-28 2018-09-28 北京师范大学 A kind of Remote Image Classification and system based on artificial bee colony algorithm
CN110083195A (en) * 2019-03-29 2019-08-02 广东工业大学 A kind of Poewr control method based on the wave-power device for improving ant colony algorithm
CN110083195B (en) * 2019-03-29 2021-08-24 广东工业大学 Power control method of wave power generation device based on improved bee colony algorithm
CN110134095A (en) * 2019-06-12 2019-08-16 国网河北能源技术服务有限公司 The method and terminal device of Power Plant Thermal analog control system optimization
CN111291855A (en) * 2020-01-19 2020-06-16 西安石油大学 Natural gas circular pipe network layout optimization method based on improved intelligent algorithm
CN111291855B (en) * 2020-01-19 2023-04-07 西安石油大学 Natural gas circular pipe network layout optimization method based on improved intelligent algorithm
CN113537439A (en) * 2020-04-17 2021-10-22 中国石油化工股份有限公司 Improved artificial bee colony optimization algorithm
CN114465216A (en) * 2022-01-17 2022-05-10 沈阳工程学院 Active power distribution network fault recovery method based on MO-CGABC

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