Juhi Optimization Algorithm (JOA)

版本 1.0.0 (4.1 KB) 作者: praveen kumar
The main inspiration of this algorithm comes from the juhi (Jasminum auriculatum)plant.
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更新时间 2025/10/6

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Mathematical Model
Let:
  • NNN = number of plants (population)
  • XiX_iXi​ = position of plant iii in the search space
  • f(Xi)f(X_i)f(Xi​) = fitness (objective function)
  • XbestX_{best}Xbest​ = best position found so far
  • ttt = current iteration, TmaxT_{max}Tmax​ = maximum iterations
Mathematical Model
Let:
  • NNN = number of plants (population)
  • XiX_iXi​ = position of plant iii in the search space
  • f(Xi)f(X_i)f(Xi​) = fitness (objective function)
  • XbestX_{best}Xbest​ = best position found so far
  • ttt = current iteration, TmaxT_{max}Tmax​ = maximum iterations
1. Root Expansion Phase (Exploration)
Plants spread roots randomly to explore nutrients:
Xinew=Xi+α⋅rand(1,D)⋅(Xrand−Xi)X_i^{new} = X_i + \alpha \cdot rand(1, D) \cdot (X_{rand} - X_i)Xinew​=Xi​+α⋅rand(1,D)⋅(Xrand​−Xi​)
where
  • α\alphaα = root expansion rate (0.5–1.0),
  • XrandX_{rand}Xrand​ = random plant position.
2. Stem Growth Phase (Transition)
Plants grow toward light and resources:
Xinew=Xi+β⋅rand(1,D)⋅(Xbest−∣Xi∣)X_i^{new} = X_i + \beta \cdot rand(1, D) \cdot (X_{best} - |X_i|)Xinew​=Xi​+β⋅rand(1,D)⋅(Xbest​−∣Xi​∣)
where
  • β\betaβ = growth factor (0.3–0.7).
3. Flower Blooming Phase (Exploitation)
Flowers bloom around the best nutrient/light zone:
Xinew=Xbest+γ⋅(randn(1,D))X_i^{new} = X_{best} + \gamma \cdot (randn(1, D))Xinew​=Xbest​+γ⋅(randn(1,D))
where
  • γ=γ0(1−tTmax)\gamma = \gamma_0 (1 - \frac{t}{T_{max}})γ=γ0​(1−Tmax​t​), decreasing over time to refine the search.
🌸 Algorithm Steps
  1. Initialize population XiX_iXi​ randomly within bounds [lb,ub][lb, ub][lb,ub].
  2. Evaluate fitness f(Xi)f(X_i)f(Xi​).
  3. Identify XbestX_{best}Xbest​.
  4. Repeat until maximum iterations:
  • Perform Root Expansion for a fraction of plants.
  • Perform Stem Growth for middle-range plants.
  • Perform Flower Blooming for top-performing plants.
  • Update XbestX_{best}Xbest​.
  1. Return XbestX_{best}Xbest​ as optimal solution.
1. Root Expansion Phase (Exploration)
Plants spread roots randomly to explore nutrients:
Xinew=Xi+α⋅rand(1,D)⋅(Xrand−Xi)X_i^{new} = X_i + \alpha \cdot rand(1, D) \cdot (X_{rand} - X_i)Xinew​=Xi​+α⋅rand(1,D)⋅(Xrand​−Xi​)
where
  • α\alphaα = root expansion rate (0.5–1.0),
  • XrandX_{rand}Xrand​ = random plant position.
2. Stem Growth Phase (Transition)
Plants grow toward light and resources:
Xinew=Xi+β⋅rand(1,D)⋅(Xbest−∣Xi∣)X_i^{new} = X_i + \beta \cdot rand(1, D) \cdot (X_{best} - |X_i|)Xinew​=Xi​+β⋅rand(1,D)⋅(Xbest​−∣Xi​∣)
where
  • β\betaβ = growth factor (0.3–0.7).
3. Flower Blooming Phase (Exploitation)
Flowers bloom around the best nutrient/light zone:
Xinew=Xbest+γ⋅(randn(1,D))X_i^{new} = X_{best} + \gamma \cdot (randn(1, D))Xinew​=Xbest​+γ⋅(randn(1,D))
where
  • γ=γ0(1−tTmax)\gamma = \gamma_0 (1 - \frac{t}{T_{max}})γ=γ0​(1−Tmax​t​), decreasing over time to refine the search.

引用格式

praveen kumar (2025). Juhi Optimization Algorithm (JOA) (https://ww2.mathworks.cn/matlabcentral/fileexchange/182214-juhi-optimization-algorithm-joa), MATLAB Central File Exchange. 检索时间: .

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JOA

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1.0.0