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 PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2] 1.) Initialize Population [Current Iteration (t) = 0] Population Size = 4; 𝑥𝑖 : (i = 1,2,3,4) and (t = 0) 𝑥1 =1.3; 𝑥2=4.3; 𝑥3=0.4; 𝑥4=−1.2 2.) Fitness Function used:

Firefly algorithm is a swarm-based metaheuristic algorithm that was introduced by Yang. Firefly Algorithm is inspired by the FLASHING Behavior of Fireflies.  Assumptions Fireflies are attracted to each other. Attractiveness is proportional to BRIGHTNESS.  Less Brighter Firefly is attracted to the Brighter Firefly. Attractiveness decrease as the distance between 2 fireflies increase. If brightness for both is the same, fireflies move randomly. New Solutions are generated by Random walks & the Attraction of fireflies. Video Link:  https://youtu.be/QvpEMR-Jp0U Firefly Optimization Algorithm Steps Initialize Parameters. Generate Population of n Fireflies. Calculate Fitness Value for Each Firefly. Check stopping criteria if (CurrentIteration := 1 to MaximumIteration ).  Update Position and Light Intensity for Each Firefly. Report the Best Solution. Initialize Parameters, Population of Fire Fly Swarm. Population Size (n) = 20; Maximum Iteration (Maxt) = 50; Dimension (d) = 10; Upper Bou