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prevent this, acceleration is xed at the core radius for particle separations less than Rc Particles that are far from one another, ie further than the particle perception limit, Rp , do not repel one another In this case ail (t) = 0, which allows particles to move towards the global best position The value of Rp will have to be optimized for each application The acceleration, ai (t), is determined for each particle before the velocity update Blackwell and Bentley suggested as electrostatic parameters, Rc = 1, Rp = 3xmax and Qi = 16 [75] Electrostatic repulsion maintains diversity, enabling the swarm to automatically detect and respond to changes in the environment Empirical evaluations of the charged PSO in [72, 73, 75] have shown it to be very e cient in dynamic environments Three types of swarms were de ned and studied: Neutral swarm, where Qi = 0 for all particles i = 1, , ns Charged swarm, where Qi > 0 for all particles i = 1, , ns All particles therefore experience repulsive forces from the other particles (when the separation is less than rp ) Atomic swarm, where half of the swarm is charged (Qi > 0) and the other half is neutral (Qi = 0) It was found that atomic swarms perform better than charged and neutral swarms [75] As a possible explanation of why atomic swarms perform better than charged swarms, consider as worst case what will happen when the separation between particles never gets below Rc If the separation between particles is always greater than or equal to Rc , particles repel one another, which never allows particles to converge to a single position Inclusion of neutral particles ensures that these particles converge to an optimum, while the charged particles roam around to automatically detect and adjust to environment changes.

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Particles with spatial extension were developed to prevent the swarm from prematurely converging [489, 877] If one particle locates an optimum, then all particles will be attracted to the optimum causing all particles to cluster closely The spatial extension of particles allows some particles to explore other areas of the search space, while others converge to the optimum to further re ne it The exploring particles may locate a di erent, more optimal solution The objective of spatial extension is to dynamically increase diversity when particles start to cluster This is achieved by adding a radius to each particle If two particles collide, ie their radii intersect, then the two particles bounce o Krink et al [489] and Vesterstr m and Riget [877] investigated three strategies for spatial extension: random bouncing, where colliding particles move in a random new direction at the same speed as before the collision;

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simple velocity-line bouncing, where particles continue to move in the same direction but at a scaled speed With scale factor in [0, 1] particles slow down, while a scale factor greater than one causes acceleration to avoid a collision A negative scale factor causes particles to move in the opposite direction to their previous movement Krink et al showed that random bouncing is not as e cient as the consistent bouncing methods To ensure convergence of the swarm, particles should bounce o on the basis of a probability An initial large bouncing probability will ensure that most collisions result in further exploration, while a small bouncing probability for the nal steps will allow the swarm to converge At all times, some particles will be allowed to cluster together to re ne the current best solution

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