RIME: A physics-based optimization

Su, Hang; Zhao, Dong; Heidari, Ali Asghar; Liu, Lei; Zhang, Xiaoqin; Mafarja, Majdi; Chen, Huiling

doi:10.1016/j.neucom.2023.02.010

Cited by 351 publications

(91 citation statements)

References 76 publications

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“…In recent years, many new SI have emerged that update the In this paper, Particle Swarm Optimization (PSO) [2], Gradient-Based Optimizer (GBO) [3], Archimedes Optimization Algorithm (AOA) [4], Corrected Archimedes Optimization Algorithm (CAOA) [5], Artificial Hummingbird Algorithm (AHA) [6], Prairie Dog Optimization (PDO) [7], Mountain Gazelle Optimizer (MGO) [8], and Rime optimization algorithm (RIME) [9] are selected. Simulation experiments are performed on different dimensions and test sets, and the results are analyzed qualitatively, quantitatively, and statistically.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of the new nearest swarm intelligent optimization algorithm for solving numerical problems

Liu,

Chen,

Cheng

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Swarm intelligence has been the research focus of optimization problems, but there is no specific description and comparison of the most suitable application dimensions and scenarios for the recent swarm intelligence optimization algorithm. In this paper, the newly emerging swarm intelligence optimization algorithm in recent years is selected for numerical simulation experiments, and qualitative and statistical analysis is carried out, and the optimal optimization algorithm in different dimensions and test set is obtained, which provides faster convergence speed and better convergence algorithm for parameter optimization researchers under different situations.

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Section: Introductionmentioning

confidence: 99%

A comparative analysis of the new nearest swarm intelligent optimization algorithm for solving numerical problems

Liu,

Chen,

Cheng

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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“…It incorporates three fundamental procedures, namely the updating rule, vector combining, and local search, to update the positions of vectors 58 . This research paper presents an algorithm named RIME, based on the physics‐based phenomenon of rime‐ice (soft‐rime and hard‐rime growth processes) 59 . This paper introduces Harris Hawks Optimizer (HHO), based on Harris hawks' cooperative behavior and hunting strategy in their natural environment, known as the surprise pounce 60 .…”

Section: Introductionmentioning

confidence: 99%

“…58 This research paper presents an algorithm named RIME, based on the physics-based phenomenon of rime-ice (soft-rime and hard-rime growth processes). 59 This paper introduces Harris Hawks Optimizer (HHO), based on Harris hawks' cooperative behavior and hunting strategy in their natural environment, known as the surprise pounce. 60 The AOA is a novel meta-heuristic technique developed by Laith et al, 61 including four basic operators: addition, subtraction, division, and multiplication.…”

Section: Introductionmentioning

confidence: 99%

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Sahay,

Upputuri,

Kumari

et al. 2023

Optim Control Appl Methods

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In this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

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“…In the past 5 years, meta-heuristic algorithms have been proposed to solve various research problems, such as Wang [14] proposed the Moth Search Algorithm (MSA) inspired by the phototaxis of moths; Heidari et al [15] proposed the Harris Hawks Optimization (HHO) algorithm inspired by the cooperation and pursuit behavior of the Harris Hawk Group; Wang et al [16] proposed the Monarch Butterfly Optimization (MBO) algorithm inspired by the migration of monarch butterflies; Li et al [17] proposed the Slime Mold Algorithm (SMA) inspired by the osculation mode of slime mold in nature; the Colony Prediction Algorithm (CPA) proposed by Tu et al [18] inspired by the corporate prediction of animals in nature; Yang et al [19] proposed the Hunger Games Search (HGS) algorithm inspired by the general purpose population based optimization technique; Ahmadianfar et al [20] proposed the weighted mean of vectors algorithm (INFO) inspired by the weighted mean idea; and Su et al [21] proposed the rime optimization (RIME) algorithm by the physical phenomenon of rime ice. However, compared to the Gray Wolf Optimization (GWO) algorithm, further research is needed on these meta heuristic algorithms in path planning problems.…”

Section: Introductionmentioning

confidence: 99%

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

Liu,

Xiao

2023

Robotica

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With the popularization of electric vehicles, early built parking lots cannot solve the charging problem of a large number of electric vehicles. Mobile charging robots have autonomous navigation and complete charging functions, which make up for this deficiency. However, there are static obstacles in the parking lot that are random and constantly changing their position, which requires a stable and fast iterative path planning method. The gray wolf optimization (GWO) algorithm is one of the optimization algorithms, which has the advantages of fast iteration speed and stability, but it has the drawback of easily falling into local optimization problems. This article first addresses this issue by improving the fitness function and position update of the GWO algorithm and then optimizing the convergence factor. Subsequently, the fitness function of the improved gray wolf optimization (IGWO) algorithm was further improved based on the minimum cost equation of the A* algorithm. The key coefficients AC1 and AC2 of two different fitness functions, Fitness1 and Fitness2, were discussed. The improved gray wolf optimization algorithm integrating A* algorithm (A*-IGWO) has improved the number of iterations and path length compared to the GWO algorithm in parking lots path planning problems.

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RIME: A physics-based optimization

Cited by 351 publications

References 76 publications

A comparative analysis of the new nearest swarm intelligent optimization algorithm for solving numerical problems

A comparative analysis of the new nearest swarm intelligent optimization algorithm for solving numerical problems

An enhanced arithmetic optimization algorithm for optimal control of reactive power

Improved gray wolf optimization algorithm integrating A* algorithm for path planning of mobile charging robots

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