Particle Swarm Optimization and Intelligence: Advances and by Konstantinos E. Parsopoulos;Michael N. Vrahatis

By Konstantinos E. Parsopoulos;Michael N. Vrahatis

Particle Swarm Optimization and Intelligence: Advances and purposes examines sleek clever optimization algorithms confirmed as very effective in functions from a number of clinical and technological fields. supplying special and specified learn, this leading edge e-book bargains a compendium of major box studies in addition to theoretical analyses and complementary recommendations important to academicians and practitioners.

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Stochastic global optimization. New York: Springer. 24 25 Chapter 2 Particle Swarm Optimization This chapter is devoted to particle swarm optimization (PSO), from early precursors to contemporary standard variants. The presentation begins with the main inspiration source behind its development, followed by early variants and discussion on their parameters. Severe deficiencies of early variants are also pointed out and their solutions are reported in a relative historical order, bringing the reader to contemporary developments, considered as the state-of-the-art PSO variants today.

We will describe two of the most common schemes, namely tournament selection and roulette-wheel selection (also called fitness proportionate selection). Tournament selection consists of a series of tournaments among a randomly selected set of individuals, adding each time one of them into the parent pool. To put it more formally, let the population, P, consist of N individuals, m be a fixed integer from the set {2, 3,…, N}, and k be the number of parents to be selected. Then, tournament selection can be described with the following pseudocode: Do (i = 1…k) Choose randomly m individuals from the population P.

Berlin: Springer. Lévy, P. (1999). Collective intelligence: Mankind’s emerging world in cyberspace. New York: Perseus Books. , & Vose, M. (1992). Characterizing crossover in genetic algorithms. Annals of Mathematics and Artificial Intelligence, 5, 27–34. 1007/BF01530778 Luenberger, D. G. (1989). Linear and nonlinear programming. Reading, MA: Addison-Wesley. McDonnell, J. , Andersen, B. , Page, W. , & Pin, F. (1992). Mobile manipulator configuration optimization using evolutionary programming. B.

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