CN113053349B - Active noise reduction method based on ant lion optimization algorithm - Google Patents
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Abstract
The invention relates to the technical field of active noise control, in particular to an active noise reduction method based on an ant lion optimization algorithm, which comprises the following steps: s100: initializing the positions of ants and ant lions as filter coefficient vectors; s200: determining an expression of a fitness function according to an active noise control system; s300: calculating the fitness of all initialized ants and ant lions, and finding the ant lion with the best fitness as an initial elite ant lion; s400: carrying out random migration of ants; s500: calculating the fitness of all ants, and updating ant lion positions; s600: updating the position of the elite ant lion; s700: and judging whether the iterative convergence condition is met, and if the iterative convergence condition is met, taking the position of the elite ant lion as an optimal filter coefficient vector. The active noise reduction method based on the ant lion optimization algorithm has the advantages of global optimization, high convergence precision and good robustness, and can effectively improve the active noise reduction performance.
Description
Technical Field
The invention relates to the technical field of active noise control, in particular to an active noise reduction method based on an ant lion optimization algorithm.
Background
With the continuous development of industrialization, noise pollution gradually becomes an important problem affecting people's life, and aiming at noise problem treatment, the noise problem treatment can be mainly divided into two categories: active noise reduction and passive noise reduction. Passive noise reduction has a poor ability to handle low frequency noise, while active noise reduction can effectively suppress low frequency noise by means of destructive interference. With the increasing sophistication of theoretical and technical solutions to various problems, the trend in ANC is towards more extensive algorithms to suppress complex noise patterns, including solutions to three-dimensional spatial and time-varying signals, etc. Guicking (1988, 1991) described its increasing interest and has now also been the result of numerous studies on ANC solutions in the market.
The ant lion Algorithm (ALO) is a biomimetic optimization algorithm. The ALO algorithm simulates the hunting mechanism of ant lions in nature. The lion larvae dig a conical hole in the sand along a circular path and throw the sand with their large chin. After digging the trap, the larvae will hide at the conical bottom, waiting for the insects/ants to get trapped in the hole. Once a lion realizes that a prey is in a trap, it will attempt to catch the prey. However, insects are usually not immediately caught and will attempt to escape the trap. In this case, the lion will intelligently throw the sand towards the edge of the hole, allowing the prey to slide to the bottom of the hole. When the prey is caught by the lower jaw of the ant lion, it is pulled underground to eat. When the ant lion eats the prey, the left vegetables can be thrown out of the hole to prepare for the next hunting.
The optimization of the filter parameters of ANC is the key to realize better noise reduction performance, and the existing related technologies have the problems of poor precision and the like, so that a new noise reduction optimization algorithm is urgently needed to improve the active noise reduction performance.
Disclosure of Invention
The invention provides an active noise reduction method based on the ant lion optimization algorithm, which has the advantages of global optimization, less adjusting parameters, high convergence precision and good robustness and can effectively improve the active noise reduction performance.
In order to solve the technical problem, the present application provides the following technical solutions:
the active noise reduction method based on the ant lion optimization algorithm comprises the following steps:
s100: initializing the positions of ants and ant lions as filter coefficient vectors;
s200: determining an expression of a fitness function according to an active noise control system;
s300: calculating the fitness of all initialized ants and ant lions, and finding the ant lion with the best fitness as an initial elite ant lion;
s400: realizing the random walk of ants according to a roulette strategy, and updating the positions of the ants;
s500: calculating the fitness of all ants, and updating the positions of the ant lions into the positions of ants with better fitness;
s600: updating the position of the elite ant lion;
s700: and judging whether the iterative convergence condition is met, and if the iterative convergence condition is met, taking the position of the elite ant lion as an optimal filter coefficient vector.
Further, the S400 includes:
s400-1: selecting one ant lion for each ant through a roulette strategy;
s400-2: updating the minimum value of the ith dimension variable in the t iteration according to the position of the ant lion according to the following formula
And the maximum value of the ith variable at the t iteration
：
Wherein the content of the first and second substances,
is the minimum value of all variables in the t iteration;
the maximum value of all variables in the t iteration;
the position of the jth ant lion in the t iteration; i is a ratio of
T is the maximum iteration number, and omega is a constant;
s400-3: position the ith ant in the t iteration
Random walks around ant and elite ant lions according to the following formula:
wherein the content of the first and second substances,
is the minimum value of the random walk of the ith dimension variable,
the maximum value of the random walk of the ith dimension variable is obtained;
s400-4: and finally, averaging and updating the positions of the ants according to the following formula:
wherein the content of the first and second substances,
to randomly walk around a lion selected according to the roulette strategy in the t-th iteration,
is a random walk around the elite lion in the t-th iteration.
Further, the S500 includes:
s500-1: and judging the adaptability of the ants according to the mean square error value calculated by the ant position vector, wherein the smaller the mean square error value is, the higher the adaptability of the ants is, and when the adaptability of the ants is higher than that of the ant lion selected by the ant lion through the roulette strategy, replacing the ant lion position.
Further, the S700 further includes: when the loop does not meet the iteration requirement, a jump-out mechanism is applied to the ant lions near the elite ant lions, and S400, S500, and S600 are repeated.
Further, the jump-out mechanism is:
wherein the content of the first and second substances,
the value of the ith dimension component of the jth lion in the tth iteration is rand which is a random number between 0 and 1, d is the distance from the elite lion to the jth lion,
in order to jump out of the threshold value,
is the minimum value of the random walk of the ith dimension variable,
is the maximum value of the random walk of the ith dimension variable.
Further, the S600 includes:
s600-1, the fitness of all ant lions under the current iteration is compared, the ant lions with the highest fitness are compared with the elite ant lions, and if the fitness is higher than the elite ant lions, the positions of the elite ant lions are replaced.
Further, each ant in S400-3 needs to calculate two random walks for position update, one around elite lion and the other around lion selected according to roulette strategy.
The technical scheme of the invention has the beneficial effects that:
in the technical scheme of the invention, the ant lion optimization algorithm and the active noise reduction algorithm are combined, the leap-out mechanism of the algorithm is improved innovatively, meanwhile, the roulette strategy and the elite strategy in the algorithm ensure the diversity of the population and the optimization performance of the algorithm, and the algorithm has the advantages of global optimization, less adjusting parameters, high convergence precision and good robustness, and can effectively improve the active noise reduction performance.
Drawings
FIG. 1 is a flowchart of an algorithm in an embodiment of an active noise reduction method based on the ant-lion optimization algorithm according to the present invention;
FIG. 2 is a diagram of a feedforward active noise control system in an embodiment of the active noise reduction method based on the ant-lion optimization algorithm of the present invention;
fig. 3 is a diagram of a feedback active noise control system in an embodiment of the active noise reduction method based on the ant-lion optimization algorithm in the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
the active noise reduction system based on the ant lion optimization algorithm of the embodiment uses an active noise reduction method based on the ant lion optimization algorithm. The system comprises a feedforward noise reduction part and a feedback noise reduction part, as shown in fig. 2, a noise signal generated by a noise source is x (n), one path passes through a primary path P (z), the other path passes through a secondary path G (z) and a feedforward filter W (z). The value of W (z) is controlled by ant lion optimization algorithm (ALO algorithm) to make the output error signal e (n) as close to 0 as possible, namely two signals are superposed to generate destructive interference, thereby realizing the feedforward noise reduction.
As shown in fig. 3, the noise signal x (n) generated by the noise source passes through the primary channel p (z) and is superimposed with the other signal to obtain the error signal e (n). Meanwhile, the error signal is superposed with the noise signal after passing through the primary channel P (z) through feedback and passing through the secondary channel G (z) and the feedforward filter W (z), and the value of W (z) is controlled through the ant lion optimization algorithm (ALO algorithm), so that the output error signal e (n) is close to 0 as much as possible, namely two signals are superposed to generate destructive interference, and thus, feedback noise reduction is realized.
As shown in fig. 1, the active noise reduction method based on the ant lion optimization algorithm of the embodiment includes the following steps:
s80: establishing an active noise control system based on an active noise reduction principle; the method specifically comprises the following steps:
s80-1: establishing a feedforward active noise control system, wherein the feedforward active noise control system comprises a primary channel response P (z), a secondary channel response G (z) and a filter response W (z), and a noise signal x (n) is synthesized into an error signal e (n) after passing through the primary channel and the secondary channel added with a filter respectively and is used as a fitness function of the lion optimization algorithm ALO. The primary channel response p (z) represents the acoustic transfer equation from the noise source to the human ear (when the headset is worn), and the secondary channel response g (z) refers to the acoustic transfer equation from the headset speaker to the human ear.
S80-2: establishing a feedback active noise control system which comprises a primary channel response P (z), a secondary channel response G (z) and a filter response W (z), wherein a synthesized error signal e (n) is used as a fitness function of the ant lion optimization algorithm ALO and is also used as a feedback signal, and the feedback signal is synthesized with a noise signal passing through a primary channel after passing through a secondary channel added with a filter.
S90: determining a fitness function expression of the ant lion optimization algorithm according to a transfer function of the active noise control system and an error signal mean square value; in this embodiment, the mean square value of the error signal is:
where N represents the number of sample points, 200 in this embodiment,
representing the mean square error value of the k-th sample point.
S100: the positions of n ants and m ant lions are initialized as filter coefficient vectors, where n and m are 300 and 100, respectively, in this embodiment.
S200: determining an expression of a fitness function according to the active noise control system, wherein the representation of the error signal in the frequency domain is:
where x (z) is a representation of the noise signal in the frequency domain.
S300: calculating the fitness of all initialized ants and ant lions, and finding out the ant lion with the best fitness as the initial elite ant lion
。
S400: and realizing the random walk of ants according to the roulette strategy, and updating the positions of the ants.
In this embodiment, S400 specifically includes:
s400-1: one ant lion is selected for each ant through a roulette strategy.
S400-2: updating the minimum value of the ith dimension variable in the t iteration according to the position of the ant lion according to the following formula
And a firstMaximum value of i-dimensional variable at t-th iteration
。
Wherein the content of the first and second substances,
is the minimum value of all variables in the t iteration;
the maximum value of all variables in the t iteration;
the position of the jth ant lion in the t iteration; i is a ratio of
(T is the maximum number of iterations, 500 in this example; ω is a constant defined by the current iteration, T in this example>ω =2, T at 0.1T>ω =3, T at 0.5T>0.75T ω =4, T>ω =5, T at 0.9T>ω =6 at 0.95T).
S400-3: position the ith ant in the t iteration
The random walk around the ant lion and elite ant lion was performed according to the following formula.
Wherein the content of the first and second substances,
is the minimum value of the random walk of the ith dimension variable,
is the maximum value of the random walk of the ith dimension variable. In S400-3, each ant needs to compute two random walks to update the location, one around elite lion and the other around lion selected according to roulette strategy.
S400-4: and finally, averaging according to the following formula to update the positions of the ants.
Wherein the content of the first and second substances,
to randomly walk around a lion selected according to the roulette strategy in the t-th iteration,
is a random walk around the elite lion in the t-th iteration.
S500: and calculating the fitness of all ants, and updating the positions of the ant lions into the positions of the ants with better fitness. And judging the adaptability of the ants according to the mean square error value calculated by the ant position vector, wherein the smaller the mean square error value is, the higher the adaptability of the ants is, and when the adaptability of the ants is higher than that of the ant lion selected by the ant lion through the roulette strategy, replacing the ant lion position.
S600: the location of the elite lion is updated. Specifically, the fitness of all the ant lions under the current iteration is compared, the ant lions with the highest fitness are compared with the elite ant lions, and if the fitness is higher than the elite ant lions, the positions of the elite ant lions are replaced.
S700: when the iterative convergence condition is met, taking the position of the elite ant lion as an optimal filter coefficient vector; when the loop does not satisfy the iterative convergence condition, a jump-out mechanism is applied to the ant lions near the elite ant lions, and S400, S500, and S600 are repeated. Specifically, in this embodiment, the exit mechanism is:
wherein the content of the first and second substances,
the value of the ith dimension component of the jth lion in the tth iteration is rand which is a random number between 0 and 1, d is the distance from the elite lion to the jth lion,
to jump out of the threshold, it is 1.50 in this embodiment. In the ant lion algorithm, the positions of ants are updated according to the positions of ant lions, and the positions of ant lions are changed along with the more optimization of the positions of the ants, so that the global search capability is insufficient, and for ANC, the noise reduction effect is possibly unsatisfactory.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (4)
1. The active noise reduction method based on the ant lion optimization algorithm is characterized by comprising the following steps: the method comprises the following steps:
s100: initializing the positions of ants and ant lions as filter coefficient vectors;
s200: determining an expression of a fitness function according to an active noise control system;
s300: calculating the fitness of all initialized ants and ant lions, and finding the ant lion with the best fitness as an initial elite ant lion;
s400: realizing the random walk of ants according to a roulette strategy, and updating the positions of the ants;
s500: calculating the fitness of all ants, and updating the positions of the ant lions into the positions of ants with better fitness;
s600: updating the position of the elite ant lion;
s700: judging whether an iteration convergence condition is met, and if the iteration convergence condition is met, taking the position of the elite ant lion as an optimal filter coefficient vector;
the S500 includes:
s500-1: judging the adaptability of the ants according to the mean square error value calculated by the ant position vector, wherein the smaller the mean square error value is, the higher the adaptability of the ants is, and when the adaptability of the ants is higher than the adaptability of the ant lion selected by the ant lion through the roulette strategy, replacing the ant lion position;
the S700 further includes: when the loop does not meet the iteration requirement, applying a jump-out mechanism to the ant lions near the elite ant lions, and repeating S400, S500 and S600; the jump-out mechanism is as follows:
wherein the content of the first and second substances,
the value of the ith dimension component of the jth lion in the tth iteration is rand which is a random number between 0 and 1, d is the distance from the elite lion to the jth lion,
in order to jump out of the threshold value,
is the minimum value of the random walk of the ith dimension variable,
is the maximum value of the random walk of the ith dimension variable.
2. The ant lion optimization algorithm-based active noise reduction method according to claim 1, wherein: the S400 includes:
s400-1: selecting one ant lion for each ant through a roulette strategy;
s400-2: updating the minimum value of the ith dimension variable in the t iteration according to the position of the ant lion according to the following formula
And the maximum value of the ith variable at the t iteration
：
Wherein the content of the first and second substances,
is the minimum value of all variables in the t iteration;
the maximum value of all variables in the t iteration;
the position of the jth ant lion in the t iteration; i is a ratio of
T is the maximum iteration number, and omega is a constant;
s400-3: position the ith ant in the t iteration
Random walks around ant and elite ant lions according to the following formula:
wherein the content of the first and second substances,
is the minimum value of the random walk of the ith dimension variable,
the maximum value of the random walk of the ith dimension variable is obtained;
s400-4: and finally, averaging and updating the positions of the ants according to the following formula:
wherein the content of the first and second substances,
to randomly walk around a lion selected according to the roulette strategy in the t-th iteration,
is a random walk around the elite lion in the t-th iteration.
3. The ant lion optimization algorithm-based active noise reduction method according to claim 1, wherein: the S600 includes:
s600-1, the fitness of all ant lions under the current iteration is compared, the ant lions with the highest fitness are compared with the elite ant lions, and if the fitness is higher than the elite ant lions, the positions of the elite ant lions are replaced.
4. The ant lion optimization algorithm-based active noise reduction method according to claim 3, wherein: in the step S400-3, each ant needs to calculate two random walks to update the position, one is a random walk around elite lion, and the other is a random walk around lion selected according to the roulette strategy.
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