Design of New Particle Swarm Optimization Algorithm and their Applications
by Jagdish Chand Bansal
Department of Mathematics
Indian Institute of Technology Roorkee, India
Abstract - Conventional computing paradigms often have difficulty dealing with real world problems, such as those characterized by noisy or incomplete data or multimodality, because of their inflexible construction. Natural systems have evolved over millennia to solve such problems, and, when closely examined, these systems often contain many simple elements that, when working together, produce complex emergent behavior. They have inspired several natural computing paradigms that can be used where conventional computing techniques perform unsatisfactorily. Particle Swarm Optimization (PSO), is a relatively recent addition to the field of natural computing.
Particle Swarm optimization technique is considered as one of the modern heuristic algorithms for optimization introduced by James Kennedy and Eberhart in 1995. It is based on the social behavior metaphor. PSO is a stochastic search technique with reduced memory requirement, computationally effective and easier to implement as compared to evolutionary algorithms (EAs). It is a simple model of social learning whose emergent behavior has found popularity in solving difficult optimization problems. It was developed based on the observations of the social behavior of animals, such as bird flocking and fish schooling, and the swarm theory.
In PSO a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually the swarm as a whole, like a flock of birds collectively foraging for food, is likely to move close to an optimum of the fitness function.
PSO has undergone many changes since its introduction. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This talk comprises a snapshot of the recent developments in the basic PSO algorithm, which have been carried out at the Department of Mathematics, Indian
Institute of Technology Roorkee, India. This talk will also focus on the applications of PSO in various optimization problems such as Optimization of Directional Overcurrent Relay Times, Optimal Design of Water Distribution Networks, Performance Analysis of Turning Process and Knapsack Problems etc.
