CN112287564B - Electrode array optimization method based on goblet sea squirt group algorithm - Google Patents
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Abstract
The invention discloses an electrode array optimization method based on a goblet sea squirt group algorithm, which comprises the steps of taking the set number of electrodes as the dimension of a search space of the goblet sea squirt group, taking the arrangement space of the electrode array as the set search space of the goblet sea squirt group, utilizing iteration of the goblet sea squirt group algorithm until the set target is met or the iteration reaches the set times, outputting the final food position of the goblet sea squirt group, and taking the final food position as the arrangement position of the electrode array to complete optimization. The electrode array optimization method based on the goblet sea squirt group algorithm has the advantages of simplicity, practicability, small calculated amount, high optimization speed, high efficiency and the like.
Description
Technical Field
The invention relates to the technical field of electrode detection, in particular to an electrode array optimization method based on a goblet sea squirt group algorithm.
Background
Personnel such as overhaul, pretest of electric power trade work in the high-voltage field, often meet the situation of operation scope peripheral equipment live working, have the hidden danger that safe distance is not enough. The common near-electricity alarm device is difficult to accurately alarm in a complex electromagnetic environment. The near-electricity alarm device based on the multiple electrodes has the advantages of high spatial resolution of electric field signals, strong anti-interference capability and the like, and can directionally detect whether equipment is electrified or not through the electrode array, so that the problems of high false alarm rate and the like of a common near-electricity alarm device are solved.
The multi-electrode spatial distribution and excitation mode is the technical key of the near-electricity alarm device, and the near-electricity alarm device is preferably optimally designed by adopting an intelligent algorithm. Intelligent algorithms are widely used in the engineering field for the solution of optimization problems, in particular non-linear optimization problems. In recent years, intelligent algorithms are applied to various sensor array designs due to the efficiency advantage of solving the nonlinear optimization problem, and genetic algorithms, particle swarm algorithms and the like are widely applied. However, the optimization process for a complex electrode array model may take several hours or even longer, and the computational cost is extremely large.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a simple and practical electrode array optimization method based on the goblet sea squirt group algorithm, which has the advantages of less calculation amount, high optimization speed and high efficiency.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an electrode array optimization method based on a goblet sea squirt group algorithm is characterized in that a set electrode number is used as a dimension of a goblet sea squirt group search space, an electrode array arrangement space is used as a set search space of the goblet sea squirt group, iteration is performed by using the goblet sea squirt group algorithm until a set target is met or the iteration reaches a set number of times, a final food position of the goblet sea squirt group is output and is used as an arrangement position of an electrode array, and optimization is completed.
The process of utilizing the goblet sea squirt group algorithm to iterate until the set target is met comprises the following steps:
s1: initializing individual number N =3n +1 of the bottle sea squirt group, taking the electrode number as the dimension number D of the search space, and setting the iteration number T max (ii) a Initializing a population to
S2: calculating the fitness of each individual of the goblet sea squirt group, taking the individual with the highest fitness as food, and dividing the rest individuals into n pilots, n trackers and n backers;
s3: updating the pilot position as follows:
wherein,
the location of the ith navigator bottle ascidian in the jth dimension, ub j And lb j Respectively a search upper limit and a search lower limit in a j-th dimension search space; f j Is the position of the food in the j-dimensional space, c 2 And c 3 Is the interval [0,1]Internally generated random numbers; parameter c 1 Is defined as:
T is the current iteration number, T max Is the maximum iteration number;
s4: update tracker location as:
in the formula,
for the location of the ith tracked individual goblet or ascidian in the jth dimension space, based on the location of the ith tracked individual goblet or ascidian in the jth dimension space>
Is the symbiotic quantity; b is a benefit parameter, and 1 or 2 is randomly selected; r is [0,1 ]]A random number of intervals;
s5: the rescuer location update is:
in the formula,
the position of the ith person of the eschewed sea squirt in the jth dimension space is represented by t, which is the current iteration number; t is a unit of max Is the maximum number of iterations;
s6: repeating the steps S2 to S5 until the iteration number reaches T max Or the food suitability value reaches a set termination threshold.
When updating the position of the ith tracker goblet ascidian individual in the jth dimension space, the position of the ith tracker goblet ascidian individual before updating in the jth dimension space is stored as
And decides->
And &>
When the degree of fitness is good or bad>
Is superior to->
Then, the location of the ith tracker goblet alone in the jth dimension space is updated to be ^ based>
The individual of goblet sea squirt group also includes a plurality of the backups, the position of the backups is updated as follows:
in the formula,
the position of the ith person of the aid goblet ascidian in the jth dimension space, and t is the current iteration number; t is max Is the maximum number of iterations.
The arrangement space of the target electrode array is a concentric electrode array, the target is set to be the minimum peak sidelobe level, and the individual fitness of each goblet ascidian is obtained by the following operation:
where theta is the angle from the normal,
is the phase shift of the electrode, j is the imaginary symbol, F max Main lobe peak, N, for concentric circular electrode array y Number of rings arranged for electrodes, N x Is the number of electrodes on the ring, r m Refers to the radius of the nth electrode (n, m) on the mth ring, and>
the feeding amplitude is that the electrodes I (n, m) are excited together, and psi (n, m) is the included angle between the electrode and the x axis.
The target electrode array arrangement space is a rectangular electrode array, the target is set to be the minimum peak sidelobe level, and the individual fitness of each goblet ascidian is obtained by the following operation:
where theta is the angle from the normal,
is the phase shift of the electrode, j is the imaginary symbol, F max Is the main lobe peak value of the concentric circle electrode array, N y Number of rings arranged for electrodes, N x Is the number of electrodes on the ring, r m Refers to the radius of the nth electrode (n, m) on the mth circular ring, and/or the device>
p mx As position of the electrode in the x direction, p my The position of the electrode in the y direction, and the feeding amplitude is I (n, m), and the electrodes are excited simultaneously.
Compared with the prior art, the invention has the advantages that:
the invention discloses an electrode array optimization method based on a goblet sea squirt group algorithm, which is characterized in that the set electrode number is used as the dimension of a search space of the goblet sea squirt group, the electrode array arrangement space is used as the set search space of the goblet sea squirt group, iteration is carried out by utilizing the goblet sea squirt group algorithm until the set target is met or the iteration reaches the set times, the final food position of the goblet sea squirt group is output and is used as the arrangement position of an electrode array, and the optimization is completed. The optimization method has the advantages of simple steps, easiness in realization, capability of meeting the requirement on the solving precision, high convergence speed and no possibility of trapping the exploration range into the local optimal solution, so that the required calculated amount is greatly reduced, the time required by the optimization process is shortened, the performance of the electrode array is improved, and the time and the cost are saved.
Drawings
FIG. 1 is a diagram of a concentric electrode array obtained by the electrode array optimization method based on the goblet sea squirt group algorithm according to the present invention;
fig. 2 is a diagram of the detection obtained by the electrode array shown in fig. 1.
Detailed Description
In order to facilitate an understanding of the invention, reference will now be made in detail to the present preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings, and the scope of the invention is not limited to the following specific embodiments.
Example (b):
in the method for optimizing the electrode array based on the goblet ascidian group algorithm of this embodiment, the set number of electrodes is used as the dimension of the search space of the goblet ascidian group, the arrangement space of the electrode array is used as the set search space of the goblet ascidian group, iteration is performed by using the goblet ascidian group algorithm until the set target is met or the iteration reaches the set number of times, the final food position of the goblet ascidian group is output and used as the arrangement position of the electrode array, and the optimization is completed. The optimization method has simple steps and is easy to realize, the solving precision can meet the requirement, the convergence speed is high, and the exploration range cannot fall into the local optimal solution, so that the required calculated amount is greatly reduced, the time required by the optimization process is shortened, and the time and the cost are saved while the performance of the electrode array is improved.
In this embodiment, the concentric electrode array is optimized to constrain the main lobe area of the directional diagram as
The target is set to minimize the peak sidelobe level, the initial target value is set to-15 dB, and if the set target value is met before the maximum number of iterations is reached, the target value is lowered by 0.5dB.
The process of utilizing the goblet sea squirt group algorithm to iterate until the set target is met comprises the following steps:
s1: initialization goblet sea squirtThe number of individuals of the group N =3n +1, N is a natural number, N generally ranges from 50 to 100, 61 in this embodiment, the number of electrodes is taken as the dimension number D of the search space, 24 in this embodiment, and the iteration number T is set max =500; initializing a population to
Initializing coordinates of each electrode in population individuals
Wherein theta is [0,2 pi ]]And a plurality of electrodes are arranged along the three concentric circular ring arrays, and the sub-distance between adjacent electrodes on the same ring is larger than a set distance.
S2: calculating the fitness of each individual of the goblet sea squirt group:
where θ is the angle from the normal,
is the phase shift of the electrode, j is the imaginary symbol, F max Is the main lobe peak value of the concentric circle electrode array, N y Number of rings arranged for electrodes, N x Is the number of electrodes on the ring, r m Refers to the radius of the nth electrode (n, m) on the mth circular ring, and/or the device>
The feeding amplitude is that all electrodes I (n, m) are excited simultaneously, psi (n, m) is the included angle between the electrodes and the x axis;
taking the individual with the maximum fitness as food, and dividing the rest individuals into n pilots, n trackers and n back-up persons;
s3: updating the pilot position as follows:
wherein,
the position of the ith navigator goblet ascidian individual in the jth dimension, ub j And lb j Respectively as the upper search limit and the lower search limit in the j-th search space, and the coordinate of the search space can be based on ^ and ^>
Obtaining m =3 when obtaining an upper limit and m =1 when obtaining a lower limit; f j Is the position of the food in the j-dimensional space, c 2 And c 3 Is the interval [0,1]Internally generated random numbers; parameter c 1 Is defined as:
T is the current iteration number, T max Is the maximum iteration number;
s4: updating the tracker position as follows:
in the formula,
for the location of the ith tracked individual Eschaea goblet in the jth dimension space, the device>
Is the symbiotic quantity; b is a benefit parameter, and 1 or 2 is randomly selected; r is [0,1 ]]A random number of intervals; when the position of the ith tracker goblet ascidian individual in the jth dimension space is updated, the position of the ith tracker goblet ascidian individual before updating is stored as ^ or ^ in the jth dimension space>
And decides->
And &>
The degree of fitness is good or bad when>
Is superior to->
Then, the location of the ith tracker goblet alone in the jth dimension space is updated to be ^ based>
S5: the rescuer location update is:
in the formula,
the position of the ith person of the eschewed sea squirt in the jth dimension space is represented by t, which is the current iteration number; t is max Is the maximum number of iterations. The introduction of cosine function by the person behind the seat makes the person behind the seat more flexible, adds 1 to make the overall distance of updating position increase, and because the maximum value of cosine function is 1, will not make the distance too big, thus, make the person behind the seat global search and local search balanced. The pilot, the follower and the back-up person are beneficial to improving the exploration performance of the algorithm and the ability of jumping out of local optimum in the later period to a certain extent.
S6: repeating the steps S2 to S5 until the iteration number reaches T max Or the food suitability value reaches a set termination threshold.
And then, the food position can be output as an electrode arrangement position, and the electrode array can meet the target requirement by arranging the electrodes according to the position. Fig. 1 is the optimized electrode array pattern, from which the detection map shown in fig. 2 is obtained, with a peak side lobe level of-17.3 dB and a narrower main lobe.
In this embodiment, when the target electrode array arrangement space is a rectangular electrode array, the target is set to minimize the peak sidelobe level, and the fitness of each individual ascidian is obtained by the following operation:
where θ is the angle from the normal,
is the phase shift of the electrode, j is the imaginary symbol, F max Is the main lobe peak value of the concentric circle electrode array, N y Number of rings arranged for electrodes, N x Is the number of electrodes on the ring, r m Refers to the radius of the nth electrode (n, m) on the mth ring, and>
p mx as position of the electrode in the x-direction, p my The position of the electrode in the y direction, and the feeding amplitude is I (n, m), and the electrodes are excited simultaneously.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-described embodiments. It should be apparent to those skilled in the art that modifications and variations can be made without departing from the technical spirit of the present invention.
Claims (4)
1. An electrode array optimization method based on a goblet sea squirt group algorithm is characterized in that: the method comprises the following steps of taking the set number of electrodes as the dimension of a searching space of a goblet sea squirt group, taking an electrode array arrangement space as the set searching space of the goblet sea squirt group, iterating by using a goblet sea squirt group algorithm until a set target is met or iteration reaches a set number of times, outputting the final food position of the goblet sea squirt group, and taking the final food position as the arrangement position of an electrode array to complete optimization;
the process of iteration by using the goblet sea squirt group algorithm until the set target is met comprises the following steps:
s1: initializing individual number N =3n +1 of the bottle sea squirt group, taking the electrode number as the dimension number D of the search space, and setting the iteration number T max (ii) a Initializing a population to
S2: calculating the fitness of each individual of the goblet sea squirt group, taking the individual with the highest fitness as food, and equally dividing the rest individuals into n pilots, n trackers and n backups;
s3: updating the pilot position as follows:
wherein,
the position of the ith navigator goblet ascidian individual in the jth dimension, ub j And lb j Respectively representing a search upper limit and a search lower limit in a j-dimension search space; f j Is the position of the food in the j-dimensional space, c 2 And c 3 Is the interval [0,1]Internally generated random numbers; parameter c 1 Is defined as:
T is the current number of iterations, T max Is the maximum iteration number;
s4: updating the tracker position as follows:
in the formula,
for the location of the ith tracked individual Eschaea goblet in the jth dimension space, the device>
Is the symbiotic quantity; b is a benefit parameter, and 1 or 2 is randomly selected; r is [0,1 ]]A random number of intervals;
s5: the rescuer location update is:
in the formula,
the position of the ith person of the aid goblet ascidian in the jth dimension space, and t is the current iteration number; t is a unit of max Is the maximum iteration number;
s6: repeating the steps S2 to S5 until the iteration number reaches T max Or the food suitability value reaches a set termination threshold.
2. The method of claim 1, wherein the method comprises the following steps: updating the jth dimension of the ith tracker-only goblet ascidian individualsWhen the space position is stored, the position of the ith tracker goblet sea squirt individual before updating in the jth dimension space is stored as
And decides->
And &>
The degree of fitness is good or bad when>
Is superior to->
Then, the location of the ith tracker goblet alone in the jth dimension space is updated to be ^ based>
3. The method of claim 1, wherein the method comprises: the arrangement space of the target electrode array is a concentric circle electrode array, the target is set to be a minimized peak sidelobe level, and the individual fitness of each goblet ascidian is obtained by the following operation:
where theta is the angle from the normal,
is the phase shift of the electrode, j is the imaginary symbol, F max Is the main lobe peak value of the concentric circle electrode array, N y Number of rings arranged for electrodes, N x Is the number of electrodes on the ring, r m Refers to the radius of the nth electrode (n, m) on the mth ring, and>
the feeding amplitude is that the electrodes I (n, m) are excited together, and psi (n, m) is the included angle between the electrode and the x axis.
4. The method of claim 1, wherein the method comprises: the target electrode array arrangement space is a rectangular electrode array, the target is set to be the minimum peak sidelobe level, and the individual fitness of each goblet ascidian is obtained by the following operation:
where theta is the angle from the normal,
is the phase shift of the electrode, j is the imaginary symbol, F max Main lobe peak, N, for concentric circular electrode array y Number of rings arranged for electrodes, N x Is the number of electrodes on the ring, r m Refers to the radius of the nth electrode (n, m) on the mth ring, and>
p mx as position of the electrode in the x direction, p my Is the position of the electrode in the y direction, and the feeding amplitude is I (n, m)And (4) exciting. />
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| WO2003105080A2 (en) * | 2002-06-10 | 2003-12-18 | Universite Libre De Bruxelles | Metapopulation genetic algorithm for combinatorial optimisation problems |
| KR20180065472A (en) * | 2016-12-08 | 2018-06-18 | 한국항공우주연구원 | Apparatus and method for pattern synthesis of antenna array |
| CN109873810A (en) * | 2019-01-14 | 2019-06-11 | 湖北工业大学 | A kind of phishing detectin method based on cup ascidian group's algorithm support vector machines |
| CN111027663A (en) * | 2019-11-12 | 2020-04-17 | 天津大学 | Method for improving algorithm of goblet sea squirt group |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2003105080A2 (en) * | 2002-06-10 | 2003-12-18 | Universite Libre De Bruxelles | Metapopulation genetic algorithm for combinatorial optimisation problems |
| KR20180065472A (en) * | 2016-12-08 | 2018-06-18 | 한국항공우주연구원 | Apparatus and method for pattern synthesis of antenna array |
| CN109873810A (en) * | 2019-01-14 | 2019-06-11 | 湖北工业大学 | A kind of phishing detectin method based on cup ascidian group's algorithm support vector machines |
| CN111027663A (en) * | 2019-11-12 | 2020-04-17 | 天津大学 | Method for improving algorithm of goblet sea squirt group |
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| Title |
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| Circular Antenna Array Synthesis Using Salp Swarm Optimization;A. DURMUŞ;《BALKAN JOURNAL OF ELECTRICAL & COMPUTER ENGINEERING》;20201007;第8卷(第4期);第320-324页 * |
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