Particle Swarm Optimization from Theory to Applications

Particle Swarm Optimization from Theory to Applications

M.A. El-Shorbagy (Department of Basic Engineering Science, Faculty of Engineering, Menoufia University, Shebin El-Kom, Egypt) and Aboul Ella Hassanien (Department of Information Technology, Faculty of Computers and Information, Cairo University, Giza, Egypt)
Copyright: © 2018 |Pages: 24
DOI: 10.4018/IJRSDA.2018040101

Abstract

Particle swarm optimization (PSO) is considered one of the most important methods in swarm intelligence. PSO is related to the study of swarms; where it is a simulation of bird flocks. It can be used to solve a wide variety of optimization problems such as unconstrained optimization problems, constrained optimization problems, nonlinear programming, multi-objective optimization, stochastic programming and combinatorial optimization problems. PSO has been presented in the literature and applied successfully in real life applications. In this paper, a comprehensive review of PSO as a well-known population-based optimization technique. The review starts by a brief introduction to the behavior of the PSO, then basic concepts and development of PSO are discussed, it's followed by the discussion of PSO inertia weight and constriction factor as well as issues related to parameter setting, selection and tuning, dynamic environments, and hybridization. Also, we introduced the other representation, convergence properties and the applications of PSO. Finally, conclusions and discussion are presented. Limitations to be addressed and the directions of research in the future are identified, and an extensive bibliography is also included.
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The Basic Of Pso

To understand the PSO, we can imagine a swarm of bees passing through an open field that contains wildflowers. The swarm has a natural desire to locate the highest density position of the flowers in this field. The swarm hasn't any previous knowledge for the field. So, the bees start their search and spread out in random locations. Every bee can remember its locations that have most flowers and this information can be transmitted by communication to the rest of the swarm. With the passage of time, the bees are torn between returning to its previously successful position in finding flowers and heading toward the location (having the most flowers) that reported by the rest of the swarm. The hesitating bee moves in both directions, converting its path to fly somewhere between the two points based on whether social influence dominates its decision or not. Occasionally, a bee may fly over a place in the field that has more flowers than had been discovered by any bee in the swarm. Then, the whole swarm would be drawn as a part in the direction of that location (see Figure 1).

In the figure, dashed lines trace the paths of our imaginary bees, and the solid arrows show their two velocity vector components. The bee No.2 was found the global best position. While, the bee No.1 has found one component of his velocity vector (its personal best location), while the other component is the global best position. The bee No. 3 shows that an example of a particle that have not found a good personal best, it is still drawn to the same position of global best.

Figure 1.

Simulation of PSO by Swarm of Bees Searching for Flowers

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