[Intelligent Optimization Algorithm] Five Elements Optimization Algorithm FPA with matlab code

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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system

Signal processing Image processing Path planning Cellular automaton Drone

Content introduction

Intelligent optimization algorithms are a method of solving complex problems by simulating biological evolution and behavior in nature. These algorithms simulate processes such as biological evolution, group behavior, and natural selection, and find the best solution through continuous iteration and optimization. Among the many methods of intelligent optimization algorithms, the Five-Phase Optimization Algorithm (FPA) is a relatively new method that has attracted much attention.

The FPA algorithm is inspired by the Five Elements Theory in ancient Chinese philosophy. The Five Elements Theory holds that everything in the universe is composed of five basic elements, namely metal, wood, water, fire and earth. These elements interact and restrict each other, forming the rules of the world. The FPA algorithm introduces elements and interactions in the Five Elements Theory into the optimization algorithm to improve the performance and effect of the algorithm.

The core idea of the FPA algorithm is to optimize by simulating the interaction between the five elements. The algorithm regards the solution space in the problem to be optimized as a space of five elements, and each solution can be expressed as a combination of five elements. By defining the attributes and interaction rules of each Five Elements element, the algorithm can simulate the mutual constraints between the elements and find the optimal solution by constantly adjusting the combination of elements.

The advantage of the FPA algorithm lies in its balance of global search capabilities for the problem and convergence speed. By introducing the interaction of five elements, the algorithm can maintain a certain diversity during the search process and avoid falling into the local optimal solution. At the same time, the algorithm can quickly converge to near the optimal solution by adjusting the combination of elements, which improves the efficiency of the algorithm.

The specific steps of the FPA algorithm are as follows:

1. Initialize the properties and interaction rules of the Five Elements elements.
2. The initial solution is randomly generated and expressed as a combination of five elements.
3. According to the evaluation index of the objective function, the fitness of the current solution is calculated.
4. According to the interaction rules of the five elements, adjust the combination of the five elements of the current solution.
5. Repeat steps 3 and 4 until the stopping condition is met (such as reaching the maximum number of iterations or finding a satisfactory solution).
6. Return the optimal solution.

The FPA algorithm has achieved good results in many practical problems. For example, in engineering optimization problems, the FPA algorithm can be used to find the best combination of design parameters to meet specific performance requirements. In machine learning and data mining, the FPA algorithm can be used to optimize the parameters of the model and improve the prediction accuracy of the model. In addition, the FPA algorithm can also be applied to image processing, signal processing, scheduling and other fields.

Although the FPA algorithm has shown many advantages in practice, it also has some challenges and limitations. First, the performance of the algorithm is highly dependent on the properties of the five elements elements and the definition of interaction rules. Different problems may require different rules, which increases the difficulty of parameter tuning of the algorithm. Secondly, the FPA algorithm may face the challenge of the curse of dimensionality when dealing with high-dimensional problems, that is, the dimensionality of the search space is too high, resulting in a decrease in the efficiency of the algorithm.

In short, the Five Elements Optimization Algorithm (FPA) is an intelligent optimization algorithm based on the Five Elements Theory. It performs global optimization by simulating the interaction between the five elements, and has better search capabilities and convergence speed. The FPA algorithm has broad application prospects in many fields, but it also needs to overcome some challenges and limitations. With the research and improvement of the FPA algorithm, I believe it will play a greater role in the future and provide more effective methods for solving complex problems.

Part of the code

function fun_plot(fun_name)

[lowerbound,upperbound,dimension,fitness]=fun_info(fun_name);

switch fun_name
    case 'F1'
        x=-100:2:100; y=x; %[-100,100]
        
    case 'F2'
        x=-100:2:100; y=x; %[-10,10]
        
    case 'F3'
        x=-100:2:100; y=x; %[-100,100]
        
    case 'F4'
        x=-100:2:100; y=x; %[-100,100]
    case 'F5'
        x=-200:2:200; y=x; %[-5,5]
    case 'F6'
        x=-100:2:100; y=x; %[-100,100]
    case 'F7'
        x=-1:0.03:1; y=x %[-1,1]
    case 'F8'
        x=-500:10:500;y=x; %[-500,500]
    case 'F9'
        x=-5:0.1:5; y=x; %[-5,5]
    case 'F10'
        x=-20:0.5:20; y=x;%[-500,500]
    case 'F11'
        x=-500:10:500; y=x;%[-0.5,0.5]
    case 'F12'
        x=-10:0.1:10; y=x;%[-pi,pi]
    case 'F13'
        x=-5:0.08:5; y=x;%[-3,1]
    case 'F14'
        x=-100:2:100; y=x;%[-100,100]
    case 'F15'
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F16'
        x=-1:0.01:1; y=x;%[-5,5]
    case 'F17'
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F18'
        x=-5:0.06:5; y=x;%[-5,5]
    case 'F19'
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F20'
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F21'
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F22'
        x=-5:0.1:5; y=x;%[-5,5]
    case 'F23'
        x=-5:0.1:5; y=x;%[-5,5]
end

    

L=length(x);
f=[];

for i=1:L
    for j=1:L
        if strcmp(fun_name,'F15')==0 & amp; & amp; strcmp(fun_name,'F19')==0 & amp; & amp; strcmp(fun_name,'F20')= =0 & amp; & amp; strcmp(fun_name,'F21')==0 & amp; & amp; strcmp(fun_name,'F22')==0 & amp; & amp; strcmp(fun_name, 'F23')==0
            f(i,j)=fitness([x(i),y(j)]);
        end
        if strcmp(fun_name,'F15')==1
            f(i,j)=fitness([x(i),y(j),0,0]);
        end
        if strcmp(fun_name,'F19')==1
            f(i,j)=fitness([x(i),y(j),0]);
        end
        if strcmp(fun_name,'F20')==1
            f(i,j)=fitness([x(i),y(j),0,0,0,0]);
        end
        if strcmp(fun_name,'F21')==1 || strcmp(fun_name,'F22')==1 || strcmp(fun_name,'F23')==1
            f(i,j)=fitness([x(i),y(j),0,0]);
        end
    end
end

surfc(x,y,f,'LineStyle','none');

end

Running results

References

Some theories are quoted from online literature. If there is any infringement, please contact the blogger to delete it

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1 Improvement and application of various intelligent optimization algorithms

Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics site selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization

2 Machine learning and deep learning

Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis

2. Image processing

Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing

3 Path planning

Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization

4 UAV application

UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories

5 Wireless sensor positioning and layout

Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization

6 Signal processing

Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization

7 Power system aspects

Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration

8 Cellular Automata

Traffic flow, crowd evacuation, virus spread, crystal growth

9 Radar

Kalman filter tracking, track correlation, track fusion

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