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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal Processing Image Processing Path Planning Cellular Automata UAV
? Content introduction
? Many optimization problems can be solved using metaheuristics rather than deterministic and heuristic methods. This study proposes a novel population-based meta-heuristic algorithm called Exponential Distribution Optimizer (EDO). The main inspiration for EDO comes from mathematics based on exponential probability distribution models. First, we initialize populations of random solutions representing multiple exponentially distributed models. The positions in each solution represent exponential random variables. The proposed algorithm includes two methods for developing and exploring strategies. During the development phase, the algorithm utilizes three main concepts: the memoryless property, the bootstrap solution, and the exponential variance between exponential random variables to update the current solution. To simulate the memoryless property, we assume that the original population contains only winners who achieve good fitness. We construct another matrix, called memoryless, to preserve newly generated solutions regardless of their fitness compared to the corresponding winners in the original population. Thus, a memoryless matrix stores two types of solutions: winners and losers. According to the memoryless property, we ignore and do not remember the previous history of these solutions, because past failures are independent and have no influence on the future. Therefore, the loser can contribute to a new solution for the next update. We select two solutions from the original population derived from the exponential distribution to update new solutions throughout the exploration phase. In addition, EDO is tested against classical test functions in addition to the Conference on Evolutionary Computation (CEC) 2014, CEC 2017, CEC 2020, and CEC 2022 benchmarks and six engineering design problems. EDO is compared with the winners of CEC 2014, CEC 2017 and CEC 2020 are L-SHADE, LSHADE?cnEpSin and AGSK respectively. EDO revealed exciting results and could be a powerful tool in the CEC competition. Statistical analysis demonstrates the superiority of the proposed EDO with 95% confidence intervals. EDO revealed exciting results and could be a powerful tool in the CEC competition. Statistical analysis demonstrates the superiority of the proposed EDO with 95% confidence intervals. EDO revealed exciting results and could be a powerful tool in the CEC competition. Statistical analysis demonstrates the superiority of the proposed EDO with 95% confidence intervals.
? Part of the code
%____________________________________________________________________________________%</code><code>% Exponential Distribution Optimizer (EDO) % %</code><code>% Developed in MATLAB R2022a %</code><code>%_______________________ ________________________________________________________________%</code><code>clc</code><code>clear all</code><code>?</code><code>NP = 30;</code><code>Max_iter=1666;</code><code>runs =30;</code><code>benchmarksType = 1;</code><code>?</code><code>if benchmarksType == 1</code><code> maxFunc = 9;</code><code>elseif benchmarksType == 2</code><code> maxFunc = 15;</code><code>elseif benchmarksType == 3</code><code> maxFunc = 27;</code><code>elseif benchmarksType == 4</code><code> maxFunc = 17;</code><code>else</code><code> exit;</code><code>end</code><code>?</code><code>time=[];</code><code>?</code><code>EDO_algor=zeros(runs, Max_iter);</code><code>EDO_algo=zeros(1,runs);</code><code>?</code><code>for fn=1:maxFunc</code><code> Function_name=strcat('F',num2str(fn));</code><code> if benchmarksType == 1</code><code> [LB,UB,Dim,F_obj]=unimodalFixedDim(Function_name);</code><code> elseif benchmarksType == 2</code><code> [LB,UB,Dim ,F_obj]=unimodalVariableDim(Function_name);</code><code> elseif benchmarksType == 3</code><code> [LB,UB,Dim,F_obj]=multimodalFixedDim(Function_name);</code><code> elseif benchmarksType == 4</code><code> [LB,UB,Dim,F_obj]=multimodalVariableDim(Function_name);</code><code> end</code><code> fprintf(['benchmark \t' ,num2str(benchmarksType),'\t','Function_name\t',Function_name]);</code><code>?</code><code> for i=1:1:runs</code><code> tic()</code><code> [EDO_BestFitness,EDO_Bestsol]=EDO(NP,Max_iter,LB,UB,Dim,F_obj);</code><code> EDO_time=toc;</code><code> time=[time;EDO_time];</code><code> EDO_algo(i)=EDO_BestFitness;</code><code>?</code><code> end</code><code> % performance measures</code><code> code><code> EDO_std=std(EDO_algo);?O</code><code> EDO_Mean=mean(EDO_algo);</code><code> EDO_best=min(EDO_algo);</code><code> EDO_worst =max(EDO_algo);</code><code>?</code><code> fprintf('\tEDO\
');</code><code> fprintf(['Best\t',num2str(EDO_best ,20),'\
']);</code><code> fprintf(['Worst\t',num2str(EDO_worst,20),'\
']);</code><code> fprintf( ['Mean\t',num2str(EDO_Mean,20),'\
']);</code><code> fprintf(['STD\t',num2str(EDO_std,20),'\
\
' ]);</code><code>?</code><code> for i=1:runs</code><code> fprintf(['\t',num2str(EDO_algo(i),20),' \
']);</code><code> end</code><code>end</code><code>?
? Running result
? References
Abdel-Basset, M., El-Shahat, D., Jameel, M., & Abouhawwash, M. (2023). Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems. Artificial Intelligence Review, 1-72.? DOI: Exponential distribution optimizer (EDO): a novel math-inspired algorithm for global optimization and engineering problems | SpringerLink
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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 location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, solution medical resource allocation optimization, facility layout optimization, field of view base station and UAV 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 forecasting, photovoltaic forecasting, battery life forecasting, radiation source identification, traffic flow forecasting, load forecasting, stock price forecasting, PM2.5 concentration forecasting, battery health status forecasting, water bodies Optical parameter inversion, NLOS signal recognition, accurate prediction of subway parking, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image stitching, image fusion, image enhancement, image compression perception
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, grid map path planning , multimodal transportation problem, vehicle cooperative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV application
UAV path planning, UAV control, UAV formation, UAV coordination, UAV task assignment
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 extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconfiguration, energy storage configuration
Aspects of 8 cellular automata
Traffic flow Crowd evacuation Virus spread Crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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