CN109345005A - A kind of integrated energy system multidimensional optimization method based on improvement whale algorithm - Google Patents
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Abstract
The invention discloses a kind of based on the integrated energy system multidimensional optimization method for improving whale algorithm, this method models integrated energy system, with the minimum objective function of overall cost of operation, using electricity, heat, cold equilibrium condition as constraint condition, the globally optimal solution of objective function is acquired using improvement whale algorithm.Wherein, improved whale algorithm is to be obtained on the basis of traditional whale algorithm by introducing self-adaptive weight sum Cauchy function.One aspect of the present invention improves the ability of searching optimum of whale algorithm, avoids and falls into local optimum；On the other hand local search ability is improved, improves convergence rate and precision, the stability having had.
Description
Technical Field
The invention relates to a multidimensional optimization method for a comprehensive energy system, in particular to a multidimensional optimization method for a cold-heat-electricity comprehensive energy system containing distributed new energy power generation based on an improved whale algorithm.
Background
In recent years, with the increasing severity of energy crisis and the maturity and perfection of distributed power supply technology, a cold-heat-electricity comprehensive energy system containing renewable energy and high energy utilization rate has also attracted much attention. The comprehensive energy system fully utilizes the characteristics of cleanness and environmental protection of renewable energy power generation, and simultaneously generates combined supply of cold, heat and electricity by taking natural gas as primary energy on the basis of the concept of energy cascade utilization. The system has high energy utilization rate and resource complementarity.
For the optimization problem of the comprehensive energy system, firstly, a system optimization operation model containing multiple energy forms needs to be established, the minimum overall operation cost is taken as an objective function, and the conversion efficiency among different energy forms and the constraint conditions of multiple types of power generation and energy storage need to be considered. And a stable and reliable high-precision optimization algorithm is adopted for solving so as to realize comprehensive utilization and collaborative optimization of energy flow and meet the requirements of various types of loads of end users.
The whale algorithm is an optimized search algorithm implemented by imitating the predation behavior characteristics of the whale in beluga recently proposed by the Australian researcher Mirjalii et al. However, the traditional whale algorithm adopts a linear inertia weight method, so that the whale algorithm is easy to fall into a local optimal solution, and has low convergence speed and low convergence precision.
Disclosure of Invention
The purpose of the invention is as follows: a comprehensive energy system multi-dimensional optimization method based on an improved whale algorithm is provided, so that the optimization problem of economic operation of a combined cold-heat-power system containing renewable energy sources is solved.
The technical scheme is as follows: the multi-dimensional optimizing method of the comprehensive energy system provided by the invention comprises the following steps: (1) establishing a model of renewable energy and cold, heat and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining a calculation mode of each operation cost and balanced conditions of electricity, heat and cold in a comprehensive energy system; wherein, each item of operation cost includes: the method comprises the following steps of (1) total operation cost of a renewable energy source unit, total gas cost, total maintenance cost of a combined cooling heating and power system (CCHP) unit, transaction cost and total operation cost of energy storage equipment; (2) calculating the overall operation cost of the comprehensive energy system based on the determined operation costs, taking the minimum overall operation cost as a target function, and taking the balance conditions of electricity, heat and cold as constraint conditions; (3) and performing multi-dimensional optimization on the objective function within a time scale of one day by adopting a whale algorithm based on a constraint condition.
Further, the step (3) comprises the following steps:
(31) randomly generating solution vectors of variables in N objective functions meeting constraint conditions; the number of solution vectors N is regarded as the number of whales; taking the variable number M in the objective function as the dimension of a whale search space; regarding the ith solution vector as the position of the ith whale in the M-dimensional space1, 2, …, N; taking the objective function value as a fitness function value;
(32) setting the maximum iteration times, and recording the maximum iteration times as itmax(ii) a Recording the current iteration number as k and initializing: k is 1; the ordinal number of the current whale is recorded as i and initialized: i is 1; the 1 st whale was considered as the optimal individual;
(33) updating the optimal individual: comparing the fitness function value of the ith whale individual with the fitness function value of the optimal individual, updating the whale individual with a small function value into the optimal individual, and keeping the optimal individual unchanged if the two are equal; recording updated optimal individual and fitness function value and position X thereofp；
(34) Update parameters ω, A, C, and l:
A＝2ω·r-ω，
C＝2·r，
where ω is a linear convergence factor, t is the current iteration number, itmaxIs the maximum number of iterations, r is [0, 1 ]]Is a random number of [ -1, 1 ]]A random number in between;
(35) updating the location of the whale with the updated parameters:
generating a random number p, if p ≧ 0.5, updating the location using the following equation:
wherein,represents the distance between the ith whale and the prey, and b is a constant for defining the shape of a logarithmic spiral;
if p <0.5 and | A | <1, then the location is updated using:
D＝|C·Xp(t)-X(t)|，
X(t+1)＝ω(t)·Xp(t)-A·D，
t is the current iteration number itmaxIs the maximum iteration number, and A.D is the surrounding step length;
if p <0.5 and | A | ≧ 1, the location is updated using the following equation:
in the formula, Xij(t) is the jth position point of the ith whale before mutation, namely the solution of the jth independent variable in the ith solution vector randomly generated in the step (31);
(36) making i equal to i +1, and judging whether i reaches N; if not, repeating the steps (33) to (35); otherwise, go to step (37);
(37) let k equal to k +1, judge whether k reaches the maximum iteration number itmax(ii) a If not, repeating the steps (33) to (36); otherwise, the algorithm is ended to obtain the updated optimal position X of the whalepWhich is the global optimal solution of the objective function.
Has the advantages that: compared with the prior art, the method introduces adaptive weight and Cauchy variation for calculation on the basis of the traditional whale algorithm when solving the global optimal solution of the comprehensive energy system. The introduction of the self-adaptive inertial weight improves the local searching capability of the traditional whale algorithm and improves the convergence precision; the positions of the whales are mutated through the Cauchy mutation operator, the global search capability of the whale algorithm is improved, and the phenomenon that the whales are trapped into partial optimality in the solution process is avoided.
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FIG. 1 is a flow chart of an embodiment of the invention providing an improved whale algorithm.
Detailed Description
The following is a detailed description of the present invention with reference to the accompanying drawings.
The invention discloses a comprehensive energy system multi-dimensional optimization method based on an improved whale algorithm, and aims to solve the optimization problem that a cold-heat-electricity combined supply comprehensive energy system achieves the lowest target of the total running cost under the condition that a cold, heat and electricity balance constraint, an energy storage device constraint and an electric-heat and electricity-cold conversion device constraint are met. The implementation steps are as follows:
step S1: establishing a model of renewable energy and cold, heat and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining a calculation mode of each operation cost and balanced conditions of electricity, heat and cold in a comprehensive energy system; wherein, each item of operation cost includes: the method comprises the following steps of (1) total operation cost of a renewable energy source unit, total gas cost, total maintenance cost of a combined cooling heating and power system (CCHP) unit, transaction cost and total operation cost of energy storage equipment;
(1) mathematical models of renewable energy and cooling, heating and power loads:
in the formula, km(t) is a variable of 0 to 1 for controlling whether renewable energy is input. CgAs a parameter of the cost of electricity generation, ComFor operating maintenance cost parameters, CdmFor downtime maintenance costs. CmAnd (t) the cost of the renewable energy unit m.The output of the renewable energy source unit m at the time period t meets the power constraint:
wherein,andrespectively is the upper limit and the lower limit of the m output of the renewable energy source unit.
(2) A system operation model:
wherein,andrespectively setting the upper limit and the lower limit of the k electric output power of the CCHP unit;andrespectively the downward ramp rate and the upward ramp rate of the electrical output power.
The waste heat boiler operation constraints in the CCHP unit k are as follows:
Hk(t)/ηHEX+Lk(t)/ηCOP≤ηFHFk(t)+ηbFk-b(t)，
wherein, ηFHη coefficient of waste heat recovery of internal combustion engine in CCHPbThermal efficiency for boiler afterburning ηHEXFor heat exchanger efficiency ηCOPIs the refrigerating coefficient of the refrigerator;the maximum operation power of a waste heat boiler in a CCHP unit k; fk(t) is the natural gas flow consumed by the internal combustion engine in the CCHP train k for a period t; fk-b(t) is n period CCHPCompensating the consumption of natural gas flow by a waste heat boiler in the unit k; hk(t) is the heat output power of a waste heat boiler heat regenerator in a CCHP unit k; l iskAnd (t) is the refrigeration output power of the refrigeration equipment in the CCHP unit k. Fk(t) and Fk-b(t) is measured by heat.
(3) Mathematical model of the energy storage device:
the operating constraints of the electrical energy storage are as follows:
0≤Pdis(t)≤Pmax，
0≤Pchar(t)≤Pmax，
SOC(t)＝SOC(t-1)+ηcharPchar(t)-Pdis(t)/ηdis，
SOCmin≤SOC(t)≤SOCmax，
wherein, Pchar(t) and Pdis(t) charging and discharging power, P, for electrical energy storage, respectivelymaxAt its maximum value, SOC (t) is the capacity of electric energy storage in the period of t, ηcharAnd ηdisCharging and discharging efficiencies for electrical energy storage, respectively; SOCminAnd SOCmaxThe minimum and maximum values of the capacity of the electrical energy storage are respectively.
The operating constraints of thermal energy storage are as follows:
HT(t)＝ηTHT(t-1)+ηTDHTD(t)-HTC(t)/ηTC，
wherein HTD(t) and HTC(t) the charging and discharging powers of the heat storage device,the maximum value of the charging power and the discharging power of the heat storage device; hT(t) heat storage level of the heat storage device for t period ηTDAnd ηTCRespectively the charging and discharging efficiencies of the heat storage device, 1- ηTThe loss rate of the heat storage device after unit time;is the heat storage capacity of the heat storage device. The operating cost of the energy storage device is expressed as follows:
Cbat(t)＝CEES(Pchar(t)+Pdis(t))+CTES(HTD(t)+HTC(t))+CCES(LTD(t)+LTC(t))，
wherein, CEES、CTESAnd CCESThe cycle loss costs of electricity, heat, and cold stored energy, respectively; l isTD(t) and LTCAnd (t) respectively charging and discharging cold power of cold energy storage.
(4) The mathematical model of the electric heating and cooling energy conversion equipment is as follows:
HEH(t)＝ηEHPEH(t)，
LEC(t)＝ηECPEC(t)，
wherein the subscripts EH and EC represent the electric-to-heat and electric-to-cold conversions, respectively, ηEHAnd ηECThe electric heat and electric cold conversion efficiency are respectively. The operating costs are as follows:
Cconv(t)＝CEHPEH(t)+CECPEC(t)。
step S2: and calculating the overall operation cost of the comprehensive energy system based on the determined operation costs, taking the minimum overall operation cost as an objective function, and taking the balance conditions of electricity, heat and cold as constraint conditions.
An objective function:
wherein, CgasIs the price per unit gas; cCCHP,lThe maintenance cost of the CCHP unit l; n is a radical ofCCHPThe number of CCHP units.
Constraint conditions are as follows:
wherein, Pbuy(t)、Psell(t) purchasing electricity and selling electric power to the upper-level power grid in the park respectively; eload(t)、 Hload(t)、Lload(t) Total Electrical, thermal and Cold loads for the campus, respectively.
Step S3: and carrying out multi-dimensional optimization on the objective function in a time scale of one day by adopting a whale algorithm based on a constraint condition.
As shown in FIG. 1, the whale algorithm adopted by the invention is an improved whale algorithm, and the general idea is as follows: firstly, calculating the fitness function value of each whale individual, and recording the optimal individual and the position X thereofp. Then, the parameters ω, a, C, l are updated. Next, a random number p is generated, if p<0.5, continue to judge if | A tint<1, the whale can swim by self-adaptive inertial weight, otherwise, the Kouchy variation can swim. If p is more than or equal to 0.5, the whale can swim in a spiral way. And judging whether the number of whales reaches the maximum value or not and reaching the maximum iteration number. If so, ending the algorithm to obtain the optimal position of the whale.
The specific steps for solving the optimal solution of the objective function based on the constraints in step S2 by using the improved whale algorithm are as follows:
(1) randomly generating solution vectors of variables in N objective functions meeting constraint conditions; the number of solution vectors N is regarded as the number of whales; taking the variable number M in the objective function as the dimension of a whale search space; regarding the ith solution vector as the position of the ith whale in the M-dimensional space1, 2, …, N; taking the objective function value as a fitness function value;
(2) setting the maximum iteration times, and recording the maximum iteration times as itmax(ii) a Recording the current iteration number as k and initializing: k is 1; the ordinal number of the current whale is recorded as i and initialized: i is 1; the 1 st whale was considered the best individual;
(3) updating the optimal individual: comparing the fitness function value of the ith whale individual with the fitness function value of the optimal individual, and comparing the whale individual with a small function valueUpdating the optimal individuals, and if the optimal individuals are equal to each other, keeping the optimal individuals unchanged; recording updated optimal individual and fitness function value and position X thereofp；
(4) Update parameters ω, A, C, and l:
A＝2ω·r-ω，
C＝2·r，
wherein omega is a linear convergence factor and is linearly reduced from 1 to 0 along with the increase of the iteration times; t is the current iteration number itmaxIs the maximum number of iterations, r is [0, 1 ]]Is a random number of [ -1, 1 ]]A random number in between;
(5) updating the location of the whale with the updated parameters:
a random number p is generated, and if p is more than or equal to 0.5, the whale walks in a spiral way and is used for simulating the whale to capture prey in a spiral motion. At this time, the position is updated using the following equation:
wherein,represents the distance between the ith whale and the prey, and b is a constant for defining the shape of a logarithmic spiral;
if p <0.5 and | A | <1, then the whale adaptive inertial weight walks. At this time, the position is updated using the following equation:
D＝|C·Xp(t)-X(t)|，
X(t+1)＝ω(t)·Xp(t)-A·D，
t is the current number of iterations,itmaxis the maximum number of iterations, and A.D is the bounding step. The mathematical description of the vesicular net predation behavior of whales includes a contraction and enclosure mechanism, which is implemented as the convergence factor ω decreases; after the convergence factor omega is introduced as the self-adaptive inertia weight, the linear inertia weight of the traditional whale algorithm is improved, and the local search capability and the convergence accuracy are improved;
if p <0.5 and | A | ≧ 1, the whale Cauchy variation walks to simulate the behavior of whale individuals searching for food randomly according to each other's location. At this time, the position is updated using the following equation:
in the formula, Xij(t) is the jth position point of the ith whale before mutation, namely the solution of the jth independent variable in the ith solution vector randomly generated in the step (1);
(6) making i equal to i +1, and judging whether i reaches N; if not, repeating the steps (3) to (5); otherwise, turning to the step (7);
(7) let k equal to k +1, judge whether k reaches the maximum iteration number itmax(ii) a If not, repeating the steps (3) to (6); otherwise, the algorithm is ended to obtain the updated optimal position X of the whalepWhich is the global optimal solution of the target function.
Claims (7)
1. A multi-dimensional optimization method for an integrated energy system is characterized by comprising the following steps:
(1) establishing a model of renewable energy and cold, heat and power loads, a system operation model, an energy storage equipment model and an energy conversion equipment model, and determining a calculation mode of each operation cost and balanced conditions of electricity, heat and cold in a comprehensive energy system; wherein, each item of operation cost includes: the method comprises the following steps of (1) total operation cost of a renewable energy source unit, total gas cost, total maintenance cost of a combined cooling heating and power system (CCHP) unit, transaction cost and total operation cost of energy storage equipment;
(2) determining the overall operation cost of the comprehensive energy system based on each operation cost, taking the minimum overall operation cost as a target function, and taking the balance conditions of electricity, heat and cold as constraint conditions;
(3) and performing multi-dimensional optimization on the objective function within a time scale of one day by adopting a whale algorithm based on a constraint condition.
2. The multi-dimensional optimization method of the integrated energy system according to claim 1, wherein in the step (1), based on the established mathematical models of renewable energy and cooling, heating and power loads,
in the formula, Cm(t) the cost of the renewable energy unit m at the time period t; cgIs a power generation cost parameter;generating capacity of the renewable energy source unit m at a time period t; comA cost parameter for operation and maintenance; k is a radical ofm(t) is a variable of 0 to 1 for controlling whether renewable energy is input; cdmMaintenance costs for shutdown;andrespectively is the upper limit and the lower limit of the output of the renewable energy unit m.
3. The multi-dimensional optimization method of integrated energy system according to claim 1, wherein in step (1), based on the established system operation model,
wherein,andrespectively setting the upper limit and the lower limit of the k electric output power of the CCHP unit;andthe downward climbing rate and the upward climbing rate of the electrical output power are respectively;
the waste heat boiler operation constraints in the CCHP unit k are as follows:
Hk(t)/ηHEX+Lk(t)/ηCOP≤ηFHFk(t)+ηbFk-b(t)，
wherein Hk(t) is the heat output power of a waste heat boiler regenerator in a CCHP unit k, ηHEXTo the heat exchanger efficiency; l isk(t) is the refrigeration output power of the refrigeration equipment in the CCHP unit k, ηCOPη being the refrigeration coefficient of the refrigeratorFHThe waste heat recovery coefficient of the internal combustion engine in CCHP; fk(t) natural gas flow consumed by the internal combustion engine in the CCHP unit k during the period t ηbThe thermal efficiency is complemented-burning for the boiler; fk-b(t) compensating the consumed natural gas flow rate of the waste heat boiler in the CCHP unit k at a time period t;the maximum operation power of a waste heat boiler in a CCHP unit k; fk(t) and Fk-b(t) is measured by heat.
4. The multi-dimensional optimization method for the integrated energy system according to claim 1, wherein in the step (1), the operation of the electrical energy storage is constrained according to the established mathematical model of the energy storage device as follows:
SOC(t)＝SOC(t-1)+ηcharPchar(t)-Pdis(t)/ηdis，
SOCmin≤SOC(t)≤SOCmax，
0≤Pdis(t)≤Pmax，
0≤Pchar(t)≤Pmax，
wherein, SOC (t) is the capacity of the electric energy storage in the period t; pchar(t) and Pdis(t) charging and discharging power, P, for electrical energy storage, respectivelymaxFor maximum value of charge-discharge power of electric energy storage ηcharAnd ηdisCharging and discharging efficiencies for electrical energy storage, respectively; SOCminAnd SOCmaxThe minimum value and the maximum value of the capacity of the electric energy storage are respectively;
the operating constraints of thermal energy storage are as follows:
HT(t)＝ηTHT(t-1)+ηTCHTC(t)-HTD(t)/ηTD，
wherein HT(t) the heat storage level of the heat storage device is 1- ηTη is the loss rate of the heat storage device after unit timeTDAnd ηTCThe heat charging efficiency and the heat discharging efficiency of the heat storage device are respectively; hTD(t) and HTC(t) the charging and discharging powers of the heat storage device are respectively;is the heat storage capacity of the heat storage device;the maximum value of the charging power and the discharging power of the heat storage device;
the operating cost of the energy storage device is expressed as follows:
Cbat(t)＝CEES(Pchar(t)+Pdis(t))+CTES(HTD(t)+HTC(t))+CCES(LTD(t)+LTC(t))，
wherein, Cbat(t) is the total operating cost of the energy storage device during the period t; cEES、CTESAnd CCESThe cycle loss costs of electricity, heat, and cold stored energy, respectively; l isTD(t) and LTCAnd (t) cold charging power and cold discharging power of cold energy storage in a time period t respectively.
5. The multi-dimensional optimization method of the integrated energy system according to claim 1, wherein in the step (1), according to the mathematical model of the electric heating and cooling energy conversion equipment,
HEH(t)＝ηEHPEH(t)，
LEC(t)＝ηECPEC(t)，
wherein HEH(t) heat quantity converted at time t, LEH(t) the cold quantity converted at the moment t; pEH(t) and PEC(t) the amount of electricity converted into heat and cold at time t, ηEHAnd ηECThe electric heating conversion efficiency and the electric cooling conversion efficiency are respectively obtained;andupper limits of the amount of electricity that can be converted into heat and cold, respectively;
the operating costs of the electric heat and electric cold energy conversion equipment are as follows:
Cconv(t)＝CEHPEH(t)+CECPEC(t)，
wherein, Cconv(t) is the total operating cost of the electric heating and cooling energy conversion equipment at the moment t; cEHAnd CECThe cost of unit electric heat and electric cold conversion respectively.
6. The method according to claim 1, wherein in step (2), if the objective function is denoted as f, then there is
Wherein, C (t) is the whole operation cost in the period t; cm(t) the cost of the renewable energy unit m at the time period t; n is a radical ofreThe total number of the renewable energy units; cgasIs the unit price of the gas; n is a radical ofCCHPThe number of units of a combined cooling heating and power system CCHP; fl(t) is the natural gas flow consumed by the internal combustion engine in the CCHP unit l during a period t; fl-b(t) compensating the consumption of natural gas flow by a waste heat boiler in a CCHP unit l at a time period t; cCCHP,lThe maintenance cost of the CCHP unit l in the period t; ctrade(t) transaction cost for t period；Cbat(t) is the total operating cost of the energy storage device during the period t; cconv(t) is the operating cost of the electric heating and cooling energy conversion equipment;
the constraint conditions are as follows:
wherein,generating capacity of the renewable energy source unit m at a time period t; pl CCHP(t) is the generated energy of the CCHP unit l in a period t; pbuy(t) and Psell(t) purchasing electricity and selling electric power to the upper-level power grid in the park respectively; pchar(t) and Pdis(t) charging and discharging power for electrical energy storage, respectively; pEH(t) and PEC(t) the electric quantities converted into heat and cold at time t, respectively; eload(t)、Hload(t) and Lload(t) total electrical, thermal and cold load for the park, respectively; hTI(t) and HTO(t) the heat storage absorption amount and the discharge amount of the heat storage device in the period of t are respectively; l isTI(t) and LTO(t) the cold storage absorption amount and the discharge amount of the cold storage device in the period of t are respectively; hl(t) is the heat output power of a waste heat boiler heat regenerator in the CCHP unit l; l isl(t) is the refrigeration output power of the refrigeration equipment in the CCHP unit l; hEH(t) heat quantity converted at time t, LEHAnd (t) is the cold energy converted at the moment t.
7. The multi-dimensional optimization method for the integrated energy system according to claim 1, wherein the step (3) comprises the steps of:
(31) randomly generating solution vectors of variables in N objective functions meeting constraint conditions; considering the number of solution vectors, N, as the number of whales; taking the variable number M in the objective function as the dimension of a whale search space; regarding the ith solution vector as the position of the ith whale in the M-dimensional space Taking the objective function value as a fitness function value;
(32) setting the maximum iteration times, and recording the maximum iteration times as itmax(ii) a Recording the current iteration number as k and initializing: k is 1; the ordinal number of the current whale is recorded as i and initialized: i is 1; the 1 st whale was considered as the optimal individual;
(33) updating the optimal individual: comparing the fitness function value of the ith whale individual with the fitness function value of the optimal individual, updating the whale individual with a small function value into the optimal individual, and keeping the optimal individual unchanged if the two are equal; recording updated optimal individual and fitness function value and position X thereofp；
(34) Update parameters ω, A, C, and l:
A＝2ω·r-ω，
C＝2·r，
where ω is a linear convergence factor, t is the current iteration number, itmaxIs the maximum number of iterations, r is [0, 1 ]]Is a random number of [ -1, 1 ]]A random number in between;
(35) updating the location of the whale with the updated parameters:
generating a random number p, if p ≧ 0.5, updating the location using the following equation:
wherein,represents the distance between the ith whale and the prey, b is a constant defining the shape of a logarithmic spiral;
if p <0.5 and | A | <1, then the location is updated using:
D＝|C·Xp(t)-X(t)|，
X(t+1)＝ω(t)·Xp(t)-A·D，
t is the current iteration number itmaxIs the maximum iteration number, and A.D is the surrounding step length;
if p <0.5 and | A | ≧ 1, the location is updated using the following equation:
in the formula, Xij(t) is the jth position point of the ith whale before mutation, namely the solution of the jth independent variable in the ith solution vector randomly generated in the step (31);
(36) making i equal to i +1, and judging whether i reaches N; if not, repeating the steps (33) to (35); otherwise, go to step (37);
(37) let k equal to k +1, judge whether k reaches the maximum iteration number itmax(ii) a If not, repeating the steps (33) to (36); otherwise, the algorithm is ended to obtain the updated optimal position X of the whalepWhich is the global optimal solution of the objective function.
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109886589A (en) * | 2019-02-28 | 2019-06-14 | 长安大学 | A method of low-carbon Job-Shop is solved based on whale optimization algorithm is improved |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108010069A (en) * | 2017-12-01 | 2018-05-08 | 湖北工业大学 | Optimize the rapid image matching method of algorithm and grey correlation analysis based on whale |
| CN108021658A (en) * | 2017-12-01 | 2018-05-11 | 湖北工业大学 | A kind of big data intelligent search method and system based on whale optimization algorithm |
| CN108112049A (en) * | 2017-12-15 | 2018-06-01 | 华中科技大学 | A kind of wireless sensor network efficiency optimization cluster-dividing method based on gam algorithm |
| CN108494015A (en) * | 2018-02-09 | 2018-09-04 | 中国科学院电工研究所 | The integrated energy system design method of one introduces a collection-lotus-storage coordination and interaction |
-
2018
- 2018-09-12 CN CN201811060521.0A patent/CN109345005A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108010069A (en) * | 2017-12-01 | 2018-05-08 | 湖北工业大学 | Optimize the rapid image matching method of algorithm and grey correlation analysis based on whale |
| CN108021658A (en) * | 2017-12-01 | 2018-05-11 | 湖北工业大学 | A kind of big data intelligent search method and system based on whale optimization algorithm |
| CN108112049A (en) * | 2017-12-15 | 2018-06-01 | 华中科技大学 | A kind of wireless sensor network efficiency optimization cluster-dividing method based on gam algorithm |
| CN108494015A (en) * | 2018-02-09 | 2018-09-04 | 中国科学院电工研究所 | The integrated energy system design method of one introduces a collection-lotus-storage coordination and interaction |
Non-Patent Citations (2)
| Title |
|---|
| 沙金霞: "改进鲸鱼算法在多目标水资源优化配置中的应用", 《水利水电技术》 * |
| 郭振洲等: "基于自适应权重和柯西变异的鲸鱼优化算法", 《微电子学与计算机》 * |
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