Golf Optimization Algorithm (Goa) attached MATLAB code

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The intelligent optimization algorithm has always been one of the research hotspots in the field of computer science and engineering.Among the many optimization algorithms, Golf Optimization Algorithm (GOA), as an emerging intelligent optimization algorithm, has attracted much attention in recent years.This article will introduce the basic principles and characteristics of the golf optimization algorithm and the application of practical problems.

In this study article, we adhere to the principle of "no free lunch" theorem and use it as a driving force to introduce a game -based innovative yuan inspiration technology, called golf optimization algorithm (GOA).GOA's structure is carefully designed and divided into two unique stages, namely exploration and development, and draw inspiration from the strategic dynamics and player behavior observed from the golf movement.By covering a comprehensive evaluation of 52 target functions and 4 actual engineering applications, the efficacy of GoA has been strictly tested.The results of the optimization process reveal the excellence of GOA in exploration and development strategies, and effectively realize the harmonious balance between the two.The comparative analysis of 10 competition algorithms shows that GOA has obvious and statistically significant advantages in a series of performance indicators.In addition, considering network elasticity, Goa's successful application of complex energy commitments highlights its ability to solve complex engineering challenges.In order to facilitate the research community, we provide the MATLAB implementation code of the Goa method proposed to ensure accessability and promote further exploration.

Golf optimization algorithm is an intelligent optimization algorithm inspired by golf competition.In the golf game, the players need to use a limited number of hitting the ball as much as possible into the hole, and the golf optimization algorithm is based on this hit process.The basic principle of algorithms is to simulate the movement of golf balls on the court, and find the optimal solution by simulating the strength and direction of hitting the ball.Compared with the traditional optimization algorithm, the golf optimization algorithm has a strong global search ability and a fast convergence speed, which can effectively apply it to solve complex optimization problems.

Golf optimization algorithm has the following characteristics: First of all, the algorithm uses a novel individual update mechanism to update the position and speed of the individual by simulating the process of hitting the ball, thereby achieving the exploration of the interpretation space.Secondly, the algorithm introduces a self -adaptive hit strategy that can dynamically adjust the parameters according to the characteristics of the problem and the state of the individual, and improve the adaptability and robustness of the algorithm.In the end, the algorithm also adopts a multi -level group collaboration mechanism, which can effectively avoid falling into the local optimal solution, thereby improving the global search ability of the algorithm.

In actual problems, the golf optimization algorithm has been widely used.For example, in the field of engineering optimization, algorithms can be used to solve problems such as structural optimization and parameter optimization; in the field of machine learning, algorithms can be used to train neural networks and optimize model parameters.Distribution, resource scheduling and other aspects.By applying in these fields, the golf optimization algorithm has achieved some remarkable results, showing high efficiency and robustness.

In general, the golf optimization algorithm, as an emerging intelligent optimization algorithm, has a strong global search ability and a fast convergence speed, which can be effectively applied to solving complex optimization problems.With the further research and improvement of the algorithm, it is believed that the golf optimization algorithm will show its strong optimization ability in more fields and provide more effective solutions for the solution of actual problems.


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1 improvement and application of various intelligent optimization algorithms
Production scheduling, economic scheduling, loading scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three -dimensional installation box, logistics site selection, freight optimization, buses optimization, charging pile layout optimization, workshop layout optimization, container shipping allocationOptimization of loading, optimization of water pump combination, optimization of the distribution of medical resources, optimization of facility layout, visual base station and drone selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), minimum daily support vector machine (LSSVM), extreme learning machine (ELM), nuclear limit learning machine (Kelm), BP, RBF, width learning, DBN, DBN, RF, RBF, DELM, XGBOOST, TCN implementation of wind power forecasting, photovoltaic prediction, battery life prediction, radiation source recognition, traffic flow forecast, load prediction, stock price prediction, PM2.5 concentration prediction, battery health state prediction, water body optical parametersAct, NLOS signal recognition, precise prediction of subway parking, diagnosis of transformer failure
2. Image processing
Image recognition, image segmentation, image detection, image hidden, image allocation, image stitching, image fusion, image enhancement, image compression sensation
3 path planning
Travel providers (TSP), vehicle path problems (VRP, MVRP, CVRP, VRPTW, etc.), drone three -dimensional path planning, drone collaboration, drone formation, robot path planning, grid map path planning, multi -formula planLianyong transportation problem, vehicle collaborative drone path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV application
Drone path planning, drone control, drone formation, drone collaboration, drone task allocation, drone safety communication trajectory online optimization
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, broadcast optimization, RSSI positioning optimization optimization optimization
6 signal processing
Signal recognition, signal encryption, signal noise, signal enhancement, radar signal processing, signal watermark embedding extraction, muscle signal, brain electrical signal, signal with time optimization
7 power system
Micro grid optimization, powerless optimization, distribution grid reconstruction, energy storage configuration
8 yuan cell automatic machine
The growth of virus diffusion crystals in traffic flow population
9 radar
Carman filtering tracking, trails associated, fusion of trails

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