SIPSO_Optimization

版本 1.0.0 (1.8 KB) 作者: praveen kumar
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Self-Interest Particle Swarm Optimization (SIPSO)
Self-Interest Particle Swarm Optimization (SIPSO) is a variation of the traditional Particle Swarm Optimization (PSO) that incorporates the concept of self-interest. Unlike standard PSO, where particles are influenced by the global best (gbestg_{\text{best}}gbest​) and local best (pbestp_{\text{best}}pbest​), SIPSO focuses more on individual particle behavior driven by self-interest to improve exploration and prevent premature convergence.
Key Features of SIPSO:
  1. Self-Interest Mechanism:Each particle prioritizes its performance and chooses to either follow its personal best or deviate significantly for exploratory purposes.
  2. Adaptive Weights:The influence of self-interest, local best, and global best is adjusted dynamically based on the iteration or swarm's diversity.
  3. Exploration vs Exploitation:Balances exploration and exploitation by assigning greater self-interest in early iterations (to explore) and gradually reducing it in later iterations (to exploit).
SIPSO Algorithm Steps:
  1. Initialization:
  • Initialize particles with random positions and velocities in the search space.
  • Evaluate the fitness of each particle and assign pbestp_{\text{best}}pbest​ and gbestg_{\text{best}}gbest​.
  1. Velocity Update:
  • Modify the velocity update rule to include a self-interest term: vi(t+1)=ωvi(t)+c1r1⋅(pbest,i−xi)+c2r2⋅(gbest−xi)+c3r3⋅SelfTermv_{i}(t+1) = \omega v_{i}(t) + c_1 r_1 \cdot (p_{\text{best},i} - x_i) + c_2 r_2 \cdot (g_{\text{best}} - x_i) + c_3 r_3 \cdot \text{SelfTerm}vi​(t+1)=ωvi​(t)+c1​r1​⋅(pbest,i​−xi​)+c2​r2​⋅(gbest​−xi​)+c3​r3​⋅SelfTerm
  • SelfTerm: A self-interest-driven direction based on the particle’s random exploration or attraction to specific regions.
  1. Position Update:
  • Update the particle position based on its new velocity: xi(t+1)=xi(t)+vi(t+1)x_i(t+1) = x_i(t) + v_i(t+1)xi​(t+1)=xi​(t)+vi​(t+1)
  1. Fitness Evaluation:
  • Evaluate the fitness of the updated particle positions.
  1. Update pbestp_{\text{best}}pbest​ and gbestg_{\text{best}}gbest​:
  • If the particle's current position is better than its pbestp_{\text{best}}pbest​, update pbestp_{\text{best}}pbest​.
  • If any particle's current position is better than gbestg_{\text{best}}gbest​, update gbestg_{\text{best}}gbest​.
  1. Termination:
  • Repeat until the maximum number of iterations or a convergence criterion is met.
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SIPSO

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1.0.0