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RIME：基于物理的优化 - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper proposes a new efficient optimization algorithm based on ice physics phenomena, called RIME or RIME Optimization Algorithm.
2. The algorithm simulates the soft-frost and hard-frost growth process to construct a soft-frost search strategy and hard-frost piercing mechanism to achieve exploration and development behavior in optimization methods.
3. Experiments were conducted to analyze the characteristics of the algorithm in finding optimal solutions, compare its performance with 10 mature algorithms and 10 improved algorithms, design parameter analysis experiments, and apply it to real engineering problems to verify its effectiveness and superiority.
Article analysis:
The article is generally reliable and trustworthy as it provides detailed information about the proposed RIME Optimization Algorithm, including its features, advantages over existing algorithms, experiments conducted to test its performance, parameter analysis experiments, and application in real engineering problems. The article also provides references for further reading on related topics such as evolutionary algorithms (EA), metaheuristic algorithms (MA), genetic algorithms (GA), differential evolution (DE), evolutionary strategies (ES), evolutionary programming (EP), gene expression programming (GEP), particle swarm optimization (PSO), Harris Hawk Optimization (HHO), Sticky Monkey Algorithm (SMA), Hunger Games Search (HGS), Grey Wolf Optimization (GWO) , Group Prey Algorithm (CPA) , Whale Optimization Algorithm (WOA) , Ant Colony Optimization (ACO) , Artificial Bee Colony (ABC) , Bat Algorithm (BA) , Cuckoo Search(CS).
However, there are some potential biases that should be noted when considering this article. Firstly, the article does not provide any counterarguments or explore any possible risks associated with using the proposed RIME Optimization Algorithm. Secondly, while the article does provide references for further reading on related topics such as EA and MA, it does not present both sides equally by providing references for other types of optimization algorithms such as linear programming or gradient descent methods. Lastly, there is a possibility of promotional content in the article as it is written by one of the authors who developed the algorithm.