Introduction to Particle Swarm Optimization Rajib Kumar - - PowerPoint PPT Presentation

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Introduction to Particle Swarm Optimization Rajib Kumar - - PowerPoint PPT Presentation

Jul 13, 2023 561 likes β€’633 views

Introduction to Particle Swarm Optimization Rajib Kumar Bhattacharjya Professor Department of Civil Engineering Indian Institute of Technology Guwahati Particle Swarm Algorithm Inspired by social behavior of bird flocking and fish schooling

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Introduction to Particle Swarm Optimization

Rajib Kumar Bhattacharjya Professor

Department of Civil Engineering Indian Institute of Technology Guwahati

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Particle Swarm Algorithm

Inspired by social behavior of bird flocking and fish schooling So what is the best strategy to locate the food? Suppose a group of birds is searching food in an area Only one piece of food is available Birds do not have any knowledge about the location of the food But they know how far the food is from their present location The best strategy is to follow the bird nearest to the food United we stand

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Particle Swarm Algorithm

A flying birds has a position and a velocity at any time 𝑒 In search of food, the bird changes his position by adjusting the velocity The changes the velocity based on his past experience and also the feedbacks received from his neighbour Current position Next position This searching process can be artificially simulated for solving non-linear

  • ptimization problem

So this is a population based stochastic

  • ptimization technique inspired by social

behaviour of bird flocking or fish schooling

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Each solution is considered as bird, called particle All the particles have a fitness value. The fitness values can be calculated using objective function All the particles preserved their individual best performance They also know the best performance of their group They adjust their velocity considering their best performance and also considering the best performance of the best particle

Particle Swarm Algorithm

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Particle Swarm Algorithm

Initialize particles Evaluate fitness of each particles Modify velocities based on previous best and global best positions Terminate criteria Next iteration STOP

π‘Š

𝑗+1 = πœ•π‘Š 𝑗 + 𝐷1 βˆ— π‘ π‘π‘œπ‘’() βˆ— 𝑄𝐢𝑗 βˆ’ π‘Œπ‘— + 𝐷2 βˆ— π‘ π‘π‘œπ‘’() βˆ— 𝐻𝐢𝑗 βˆ’ π‘Œπ‘—

π‘Œπ‘—+1 = π‘Œπ‘— + π‘Š

𝑗+1

Velocity is updated Position is updated Inertia effect Local search, personal influence Global search, Social influence 𝐷1 and 𝐷2 are the learning factor πœ• is the inertia weight

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Particle Swarm Algorithm

π‘Œπ‘— 𝑄𝐢𝑗 𝐻𝐢𝑗 π‘Š

𝑗

π‘Œπ‘—+1 Personal best performance Best performance

  • f the group