CN104166877A - Microgrid optimization operation method based on improved binary system particle swarm optimization algorithm - Google Patents
Microgrid optimization operation method based on improved binary system particle swarm optimization algorithm Download PDFInfo
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Abstract
The invention relates to a microgrid optimization method based on an improved binary system particle swarm optimization algorithm. For a microgrid containing various micro power sources, taking start-stop strategies of controllable micro power sources into account, a microgrid optimization operation mathematical model considering economic costs and environmental benefits is established. An improved binary system particle swarm optimization algorithm based on a catfish effect is provided, output of the micro power sources in the microgrid in each period of time is solved under different control strategies. A specific microgrid including wind, light, a micro gas turbine, a fuel cell and a diesel generator is used as an embodiment, and embodiment analysis is performed.
Description
Technical field
The present invention relates to microgrid optimization operation, relate in particular to a kind of microgrid optimizing operation method based on improving Binary Particle Swarm Optimization.
Background technology
Due to energy crisis and environmental protection problem, comprise that the distributed power generation of new forms of energy and regenerative resource receives publicity day by day in electric power energy industry.Microgrid, as the effective technology means of the integrated of distributed power generation and access, has improved dirigibility, controllable type and the economy of distributed power source, becomes the important technology that intelligent grid is built.
The optimization operation of microgrid is complicated non-linear a, multiple constraint, multiobject optimization problem, and traditional derivation algorithm can not meet and solves needs, and one adopts intelligent optimization algorithm to solve.Scale-of-two particle group optimizing (Binary Particle Swarm Optimization, BPSO) algorithm has the advantages such as dirigibility is large, algorithm is easily realized, restrain fast, need adjustment parameter is few, on the optimization operation study of microgrid, is widely used.For this algorithm, there is population diversity poor, easy Premature Convergence, be absorbed in the shortcoming of locally optimal solution, Chinese scholars has proposed corresponding improvement algorithm, algorithm as complete in genetic particle, simulated annealing particle cluster algorithm, Fuzzy particle swarm optimization, Bee Evolutionary particle cluster algorithm etc., but these algorithms have only improved the position of single particle or part particle mostly, and the flying method of whole population is not revised.
Summary of the invention
When the present invention will overcome application binary PSO Algorithm microgrid optimization operation problem, population is easily absorbed in the drawback of locally optimal solution, introduces " Catfish Effect " in economics, improves traditional binary particle swarm optimization algorithm, is applied to microgrid optimization operation.
It is target that microgrid cost of electricity-generating and Environmental costs minimum are take in the present invention, consider voltage out-of-limit, power-balance, micro-power supply restriction, the micro-power supply constraint conditions such as Power Limitation of climbing of exerting oneself, set up multiple constraint Non-linear Optimal Model, and a kind of improvement Binary Particle Swarm Optimization with Catfish Effect proposed, solving the micro-power supply of day part exerts oneself, for specific embodiment, optimum results has been carried out analyzing relatively.
Technical scheme of the present invention is:
Step 1, uncontrollable type power supply in microgrid is carried out to generated power forecasting, the load power stack that the generated output negative value of trying to achieve and prediction are obtained obtains net load power;
Step 2, the micro-power supply of controllable type is exerted oneself and carried out mathematical modeling, consider its on off control scheme, exerted oneself as optimized variable;
Step 3, to take microgrid cost of electricity-generating minimum and Environmental costs minimum be target, considers different microgrid operation strategies, sets up microgrid optimal operation model;
Step 4, " Catfish Effect " in economics introduced in Binary Particle Swarm Optimization, tried to achieve the microgrid that meets constraint condition and optimize operating scheme.
Compare with background technology, the beneficial effect that the present invention has is:
(1) whether controlled according to micro-output power of power supply, adopt different control strategies.The micro-power supply of uncontrollable type is made to " bearing " load and process, the generated output negative value of trying to achieve and load power stack are obtained to net load power.For the micro-power supply of controllable type, considered the on off control of the micro-power supply of controllable type, adopt binary coding, accelerated model solution speed.
(2) on solving model, adopt improved Binary Particle Swarm Optimization.By the speed of traditional BP SO more new formula know, when population is absorbed in locally optimal solution, particle is all gathered near locally optimal solution, distance is between the two very little, speed is not almost revised, when next iteration, particle will be almost not mobile so.For improving this defect, in traditional BP SO particle population, introduce Catfish Effect.
Accompanying drawing explanation
Fig. 1 is that the microgrid based on improving Binary Particle Swarm Optimization is optimized operational flow diagram;
Fig. 2 is microgrid structural drawing of the present invention;
Fig. 3 is embodiment of the present invention area typical case's day wind power generating set, photovoltaic cell capable of generating power prediction curve figure;
Fig. 4 is embodiment of the present invention regional load and net load curve map;
Fig. 5 is electricity price level and micro-power supply integrated cost-output power figure in the embodiment of the present invention;
Fig. 6 is that the present invention optimizes rear microgrid at the strategy optimum results figure of 24 hours once;
Fig. 7 is that the present invention optimizes rear microgrid at the strategy optimum results figure of two times 24 hours;
Fig. 8 is that the present invention optimizes rear microgrid at the strategy optimum results figure of three times 24 hours.
Specific embodiments
A kind of microgrid optimizing operation method process flow diagram based on improvement Binary Particle Swarm Optimization of the present invention as shown in Figure 1, comprises the steps:
(1) gather microgrid information on load data, weather information data, the historical data of comprehensive microgrid operation, to carrying out the prediction of following a day, obtains load, wind energy and the solar power predicted data of microgrid in following a day, calculates net load power;
(2) add up the micro-power supply characteristic of microgrid, set up the mathematical model of exerting oneself of the micro-power supply of all controllable types in microgrid;
(3) the following intraday economical operation of microgrid is divided into 24 periods, take microgrid 24 hour operation cost of electricity-generating and Environmental costs minimum is objective function, day part power-balance, the node voltage of considering microgrid inside retrains, the restriction/ramp-rate limits/start-up and shut-down costs of exerting oneself of each equipment component, sets up microgrid optimal operation model;
(4) the net load power data based in step (2), adopt the improved Binary Particle Swarm Optimization mathematical model that microgrid optimization lower, in step (3) moves to difference operation strategy to solve, obtain the Unit Commitment of the micro-power supply of day part controllable type and the prioritization scheme of exerting oneself.
One, above-mentioned net load power solve as follows:
（1）
Wherein, subscript t is illustrated in the t period; P
netfor net load power, P
loadfor microgrid total load power, P
ucibe i the micro-power supply of uncontrollable type, MS
ucfor the micro-power supply collection of uncontrollable type.
Two, the micro-power supply of the above-mentioned controllable type mathematical model of exerting oneself is as follows:
1, the diesel-driven generator model of exerting oneself:
（2）
Wherein, C
fDEfuel cost for diesel-driven generator; P
dEfor diesel-driven generator output power; A, b, c are parameter, and one is given by manufacturer for size, and the present invention chooses a=6, b=0.012, c=0.00085.
2, the miniature gas turbine model of exerting oneself:
（3）
Wherein, C
fMTthe fuel cost that represents miniature gas turbine; P
mToutput power for miniature gas turbine; c
ngprice for rock gas; LHV
nglow-heat calorific value for rock gas; η
mTefficiency for miniature gas turbine; Δ t is a period of microgrid operation, and the present invention be take one hour as optimizing the period.
3, the miniature gas turbine model of exerting oneself:
（4）
Wherein, C
fFCthe fuel cost that represents fuel cell; P
fCthe output power that represents fuel cell; η
fCrepresent fuel cell efficiency.
Three, above-mentioned objective function is as follows:
The present invention mainly considers economy and the feature of environmental protection of microgrid operation, and, Environmental costs minimum target function minimum with microgrid cost of electricity-generating (comprising fuel cost, operation expense and micro-power supply start-up and shut-down costs), set up microgrid optimal operation model
（5）
Wherein, C
gfor microgrid cost of electricity-generating; C
efor microgrid Environmental costs; C
gridfor microgrid is to major network power purchase cost or sale of electricity income; T is microgrid optimization cycle hop count when total; I
ibe the startup-shutdown state of i micro-power supply, 1 represents open state, and 0 represents stopped status; C
fiit is the fuel used to generate electricity cost of i micro-power supply; P
ithe output power that represents i micro-power supply; N is the number of micro-power supply; k
ithe operation expense coefficient that represents i micro-power supply; c
onithe startup-shutdown unit price that represents i micro-power supply; λ
ijit is the coefficient that i micro-power supply discharges j kind pollutant; c
jit is the environmental value of j kind pollutant; p
jbe the fine quantity of j kind pollutant; M is the kind number of pollutant; P
gridfor the through-put power between microgrid and major network, be worth for timing represents that microgrid is to major network power purchase power, be worth and represent that microgrid is to major network sale of electricity power when negative; c
buyand c
sellbe respectively power purchase and sale of electricity electricity price.
Four, above-mentioned constraint condition is as follows:
(1) power-balance constraint
（6）
（7）
Wherein, P
lossfor microgrid network loss; P
k, Q
kbe respectively meritorious, the reactive power of the transmission of k bar branch road; N
bfor branch road sum; R
kbe the resistance of k bar branch road; U
kbe the voltage of k bar branch road.
(2) working voltage constraint
（8）
Wherein, i represents i micro-power supply; U is node voltage, U
maxand U
minbe respectively economize on electricity and press upper and lower limit.
(3) micro-output power of power supply constraint
（9）
Wherein, P
maxand P
minbe respectively the upper and lower limit of micro-output power of power supply.
(4) micro-power supply climbing rate constraint
（10）
Wherein, r
upand r
downbe respectively climbing and lower creep speed in the permission maximum of micro-power supply active power of output.
(5) micro-power supply start and stop number of times and start-stop time constraint
（11）
Wherein, M allows maximum start-stop time t in optimization cycle T
onand t
offbe respectively the shortest and open, stop time.
(6) microgrid and the constraint of major network through-put power
（12）
Wherein, P
grid maxand P
grid minbe respectively the upper and lower limit of microgrid and major network permission through-put power.
Five, above-mentioned microgrid operation strategy is as follows:
According to micro-power supply power interactive mode between priority scheduling and microgrid and major network whether, adopt three kinds of operation strategies:
Strategy one: preferentially utilize micro-power supply to meet the workload demand in microgrid, while not meeting workload demand from major network absorbed power, but cannot be to major network output power;
Strategy two: micro-power supply and major network participate in the operation optimization of system jointly, being still can be from major network absorbed power, cannot be to major network output power;
Strategy three: microgrid and major network can free two-way exchange power, microgrid both can be from major network absorbed power, also can during lower than major network, by unnecessary electricity, sell major network in cost of electricity-generating.
Six, above-mentioned improvement binary particle swarm algorithm implementation procedure is as follows:
1, binary particle swarm algorithm mathematical description
Binary particle swarm algorithm is to propose on the basis of basic particle group algorithm, is applicable to discrete space optimization problem.In BPSO, the more new formula of speed is identical with PSO, but the velocity vector of particle is no longer the rate of change of particle position, but the probability that particle position changes represents the position of particle determines it is 1 or 0 with this probability.The more new formula of speed and position is as follows
（13）
In formula:
（14）
Wherein, subscript k represents iteration the k time; v
ij, x
ijbe illustrated respectively in the solution space of D dimension i speed and the position that particle is tieed up at j; p
ijrepresent the optimal location that i particle self searches; p
gj represents the global optimum position that whole population searches; c
1, c
2being the study factor, is two normal numbers; r
1, r
2, r
3it is the random number between [0,1].
2, introduce " Catfish Effect "
Catfish Effect is exactly that the whole colony that excitation is absorbed in inertia recovers and maintains vigour by introducing competitive individuality.For particle cluster algorithm, when particle is gathered in local optimum and while causing search to be stagnated, structure " catfish particle " removes to drive " the sardine particle " that is absorbed in local optimum position, utilizes Catfish Effect make population to jump out local extremum and find global optimum.This step has following three committed steps:
(1) judge whether to introduce " catfish particle ".When whole population is absorbed in locally optimal solution, when weakening greatly, introduces population diversity, and utilize following formula to quantize the diversity of population
（15）
Wherein, PopDiv is Diversity of population; PopSize is Population Size; α is relative Diversity of population; Iter
maxfor maximum iteration time.
(2) structure " catfish particle ".The individual particle configuration of R (R=10%*PopSize) of getting fitness value maximum in population becomes " catfish particle ", upgrades at random the position of this R particle in solution space.
(3) " catfish particle " driving " sardine particle ".Calculate " catfish particle " fitness value of neotectonics, when fitness value reduces (" catfish particle " is more vigourous), drive " the sardine particle " that be absorbed in locally optimal solution and there is inertia, according to following formula, upgrade " sardine particle " speed
（16）
Wherein, c
cebeing the Catfish Effect study factor, is normal number; r
ceit is the random number between [0,1]; S
icbe the state that i " sardine particle " driven by c " catfish particle ", 1 represents to be activated, and 0 represents not driven; x
cjit is c " catfish particle " position after the random renewal of step (2); Fitness () is fitness value.
3, the concrete implementation step of microgrid optimizing operation method based on improving Binary Particle Swarm Optimization is as follows:
(1) initialization: select microgrid operation strategy; Input micro-power supply, load parameter etc.; Input algorithm parameter (maximum iteration time, dimensionality of particle, Population Size, flying speed bound, Diversity of population threshold values etc.).
(2) iterations iter=1 is set.
(3) calculate the fitness value of each particle, recording minimum fitness value is globally optimal solution fbest, note particle position global extremum point gbest and individual extreme point pbest.
(4) judge whether current iterations reaches maximum iteration time, if reach, finishes algorithm, output result of calculation; If do not meet and set iterations iter=iter+1.
(5) judge whether to introduce " catfish particle ".When whole population is absorbed in locally optimal solution, population diversity will weaken greatly, while being attenuated to threshold values, go to step (6); Otherwise go to step (8), utilize following formula to quantize the diversity of population
（17）
Wherein, PopDiv is Diversity of population; PopSize is Population Size; α is relative Diversity of population; Iter
maxfor maximum iteration time.
(6) structure " catfish particle ".Take out R particle configuration of fitness value maximum in population and become " catfish particle " (the present invention gets R=10%*PopSize), upgrade at random the position of this R particle in solution space.Remaining particle is called " sardine particle ".
(7) " catfish particle " driving " sardine particle ".Calculate " catfish particle " fitness value of neotectonics, when fitness value increase or constant, go to step (8); Otherwise (" catfish particle " is more vigourous), drives " the sardine particle " that be absorbed in locally optimal solution and have inertia, according to formula following formula, upgrades " sardine particle " speed
（18）
Wherein, c
cebeing the Catfish Effect study factor, is normal number; r
ceit is the random number between [0,1]; S
icbe the state that i " sardine particle " driven by c " catfish particle ", 1 represents to be activated, and 0 represents not driven; x
cjit is c " catfish particle " position after the random renewal of step (2); Fitness () is fitness value.
(8) according to formula (6) more position and the speed of new particle.
(9) whether the state that judges particle meets all kinds of inequality constrain conditions, if meet, retains particle position, if do not meet and get limit value.Go to step (3).
Seven, embodiment:
Consider microgrid as shown in Figure 2, be wherein kept closed with the public lotus root chalaza PPC of outside major network, microgrid is operated under the pattern of being incorporated into the power networks.In system, micro-power supply has diesel-driven generator (DE), miniature gas turbine (MT), fuel cell (FC), wind-power electricity generation (WT), photovoltaic cell (PV).The meritorious upper limit of major network and microgrid transmission is got 100kW, get-20kW of lower limit.The micro-power parameter of Environmental costs parameter and controllable type respectively as shown in Table 1 and Table 2.Carry out tou power price policy, tou power price standard is as shown in table 3.Adopt the present invention to carry out Real time optimal dispatch to the microgrid under difference operation strategy.
Table 1 Environmental costs parameter
The micro-power parameter of table 2 controllable type
Table 3 tou power price
(1) gather microgrid information on load data, weather information data, the historical data of comprehensive microgrid operation, to carrying out the prediction of following a day, obtain load, wind energy and the solar power predicted data of microgrid in following a day, calculate net load power, embodiment area typical case's day wind power generating set and photovoltaic cell capable of generating power prediction curve figure, load and net load curve map are respectively as shown in Figure 3, Figure 4;
(2) add up micro-power supply operation characteristic in microgrid, set up the cost-output power function of the micro-power supply of all controllable types in microgrid, obtain micro-power supply integrated cost-output power curve as shown in Figure 5.Take one hour as optimizing the period, can adopt and improve the plan for start-up and shut-down that binary particle swarm algorithm solves the micro-power supply of each controllable type;
(3) the following intraday economical operation of microgrid is divided into 24 periods, take microgrid 24 hour operation cost of electricity-generating and Environmental costs minimum is objective function, consider day part power-balance, node voltage constraint, the restriction of exerting oneself of each equipment component, ramp-rate limits, the start-up and shut-down costs of microgrid inside, set up microgrid optimal operation model;
(4) the net load power data based in step (2), adopt improved Binary Particle Swarm Optimization to solve moving the mathematical model of the microgrid optimization operation in the step (3) under strategy one, two, three, obtain the Unit Commitment of the micro-power supply of day part controllable type and exert oneself prioritization scheme as shown in Fig. 6-8, microgrid operating cost is as shown in table 4.
Table 4 microgrid operating cost
According to operation strategy one, microgrid only when workload demand exceeds micro-power supply and exerts oneself the 11-12 of the upper limit to major network power purchase.By Fig. 5, known, when output power surpasses 22kW, MT comprehensive electric generating cost is the highest, and FC takes second place, and DE is minimum.Therefore, DE almost remains on and completely sends out state; FC is better than MT generating, also roughly reaches full and sends out; And except shutting down when the lower 1-4 of load level and during 23-24, MT output power variation tendency and workload demand variation tendency are roughly the same.
Tactful two times of operation, major network also can participate in optimizing.By Fig. 5, known, when output power is greater than 23kW, DE comprehensive electric generating cost is minimum, and lower than purchase electricity price, therefore DE is always in completely sending out state; When paddy, MT, FC comprehensive electric generating cost are all higher than purchase electricity price, and shut down in former capital, from major network power purchase; When at ordinary times with peak, FC comprehensive electric generating cost is lower than electricity price, Gu Manfa; When peak, MT comprehensive electric generating cost is lower than electricity price, therefore generate electricity according to workload demand; When 11-12, workload demand surpasses micro-power supply upper limit of exerting oneself, therefore after all micro-power supplys completely send out, also need be from major network power purchase.
Compare operation strategy two, three times micro-power supplys of operation strategy can multiple electricity to major network sale of electricity.By Fig. 5, known, sale of electricity electricity price when all micro-power supply integrated costs are all lower than electrical network peak when peak, therefore whole full sending out meets larger workload demand except generating when the 11-12 is preferential, when peak, all the other are constantly all to electrical network sale of electricity.
As shown in Table 4, move tactful three optimums, operation strategy two takes second place, and operation strategy one is the poorest.Because tactful three times of operation, microgrid can play " peak load shifting " effect, and sale of electricity income can reduce microgrid operating cost.From table 4, also can find out, in microgrid, to account for the proportion of overall running cost lighter for Environmental costs simultaneously, and this is because rock gas on the high side at present, and pollutant emission fine is lower.In view of the current growing interest to environmental protection, should improve fine to encourage clean energy resource generating.
In sum, by the test result of the present embodiment, illustrate that a kind of microgrid optimizing operation method based on improving Binary Particle Swarm Optimization that the present invention proposes can effectively realize the optimization operation of microgrid, give full play to economic benefit, the environmental benefit of microgrid.
Claims (4)
1. the microgrid optimizing operation method based on improving Binary Particle Swarm Optimization, is characterized in that, comprises the steps:
(1) gather microgrid information on load data, weather information data, the historical data of comprehensive microgrid operation, to carrying out the prediction of following a day, obtains load, wind energy and the solar power predicted data of microgrid in following a day, calculates net load power;
(2) add up the micro-power supply characteristic of microgrid, set up the mathematical model of exerting oneself of the micro-power supply of all controllable types in microgrid;
(3) the following intraday economical operation of microgrid is divided into 24 periods, take microgrid 24 hour operation cost of electricity-generating and Environmental costs minimum is objective function, day part power-balance, the node voltage of considering microgrid inside retrains, the restriction/ramp-rate limits/start-up and shut-down costs of exerting oneself of each equipment component, sets up microgrid optimal operation model;
(4) the net load power data based in step (2), adopt improved Binary Particle Swarm Optimization to solve the mathematical model of the microgrid optimization operation in the step (3) under difference operation strategy, obtain the Unit Commitment of the micro-power supply of day part controllable type and the prioritization scheme of exerting oneself.
2. method according to claim 1, is characterized in that, calculates the net load of described microgrid, and described net load is the generated output that load power deducts aerogenerator and photovoltaic cell, is calculated as follows
Wherein, subscript t is illustrated in the t period; P
netfor net load power, P
loadfor microgrid total load power, P
ucibe i the micro-power supply of uncontrollable type, MS
ucfor the micro-power supply collection of uncontrollable type.
3. method according to claim 1, is characterized in that, the microgrid optimal operation model in step (3) is as follows, and wherein objective function is
Wherein, C
gfor microgrid cost of electricity-generating, C
efor microgrid Environmental costs, C
gridfor microgrid is to major network power purchase cost or sale of electricity income, T is microgrid optimization cycle hop count when total, I
ibe the startup-shutdown state of i micro-power supply, 1 represents open state, and 0 represents stopped status, C
fibe the fuel used to generate electricity cost of i micro-power supply, P
ithe output power that represents i micro-power supply, the number that N is micro-power supply, k
ithe operation expense coefficient that represents i micro-power supply, c
onithe startup-shutdown unit price that represents i micro-power supply, λ
ijbe the coefficient that i micro-power supply discharges j kind pollutant, c
jbe the environmental value of j kind pollutant, p
jbe the fine quantity of j kind pollutant, the kind number that M is pollutant, P
gridfor the through-put power between microgrid and major network, be worth for timing represents that microgrid is to major network power purchase power, be worth and represent that microgrid is to major network sale of electricity power, c when negative
buyand c
sellbe respectively power purchase and sale of electricity electricity price;
Constraint condition is
(1) power-balance constraint
Wherein, P
lossfor microgrid network loss, P
k, Q
kbe respectively meritorious, the reactive power of the transmission of k bar branch road, N
bfor branch road sum, R
kbe the resistance of k bar branch road, U
kbe the voltage of k bar branch road; (2) working voltage constraint
Wherein, i represents i micro-power supply, and U is node voltage, U
maxand U
minbe respectively economize on electricity and press upper and lower limit;
(3) micro-output power of power supply constraint
Wherein, P
maxand P
minbe respectively the upper and lower limit of micro-output power of power supply;
(4) micro-power supply climbing rate constraint
Wherein, r
upand r
downbe respectively climbing and lower creep speed in the permission maximum of micro-power supply active power of output;
(5) micro-power supply start and stop number of times and start-stop time constraint
Wherein, M allows maximum start-stop time t in optimization cycle T
onand t
offbe respectively the shortest and open, stop time;
(6) microgrid and the constraint of major network through-put power
Wherein, P
grid maxand P
grid minbe respectively the upper and lower limit of microgrid and major network permission through-put power.
4. method according to claim 1, is characterized in that, the algorithm characteristics of mentioning in step (4) is, following step, consists of:
(1) initialization: select microgrid operation strategy; Input micro-power supply, load parameter etc.; Input algorithm parameter (maximum iteration time, dimensionality of particle, Population Size, flying speed bound, Diversity of population threshold values etc.);
(2) iterations iter=1 is set;
(3) calculate the fitness value of each particle, recording minimum fitness value is globally optimal solution fbest, note particle position global extremum point gbest and individual extreme point pbest;
(4) judge whether current iterations reaches maximum iteration time, if reach, finishes algorithm, output result of calculation; If do not meet and set iterations iter=iter+1;
(5) judge whether to introduce " catfish particle ", when whole population is absorbed in locally optimal solution, population diversity will weaken greatly, while being attenuated to threshold values, go to step (6), otherwise go to step (8), utilize following formula to quantize the diversity of population
Wherein, PopDiv is Diversity of population; PopSize is Population Size; α is relative Diversity of population; Iter
maxfor maximum iteration time;
(6) structure " catfish particle ", take out R particle configuration of fitness value maximum in population and become " catfish particle " (the present invention gets R=10%*PopSize), the random position of this R particle in solution space of upgrading, remaining particle is called " sardine particle ";
(7) " catfish particle " driving " sardine particle ", calculates " catfish particle " fitness value of neotectonics, when fitness value increase or constant, goes to step (8); Otherwise (" catfish particle " is more vigourous), drives " the sardine particle " that be absorbed in locally optimal solution and have inertia, according to following formula, upgrades " sardine particle " speed
Wherein, c
cebeing the Catfish Effect study factor, is normal number; r
ceit is the random number between [0,1]; S
icbe the state that i " sardine particle " driven by c " catfish particle ", 1 represents to be activated, and 0 represents not driven; x
cjit is c " catfish particle " position after the random renewal of step (2); Fitness () is fitness value;
(8) according to formula following formula more position and the speed of new particle
(9) whether the state that judges particle meets all kinds of inequality constrain conditions, if meet, retains particle position, if do not meet and get limit value, goes to step (3).
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