Particle Swarm Optimization and Intelligence: Advances and Applications
Since its initial development, particle swarm optimization has gained wide recognition due to its ability to provide solutions efficiently, requiring only minimal implementation effort. Particle Swarm Optimization and Intelligence: Advances and Applications examines modern intelligent optimization algorithms proven as very efficient in applications from various scientific and technological fields. Providing distinguished and unique research, this innovative publication offers a compendium of leading field experiences as well as theoretical analyses and complementary techniques useful to academicians and practitioners.
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Particle Swarm Optimization
Theoretical Derivationsand Application Issues
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2-dimensional analysis application of PSO Bartz-Beielstein best particle book at hand ç ç ç chapter Clerc Coello component concepts considered constraints constriction coefficient convergence corresponding defined deflection denoted detected dynamic Eberhart efficient equation evaluations evolution strategies evolutionary algorithms Evolutionary Computation experiments function value fuzzy cognitive maps genetic algorithms global minimizer global optimization IGI Global inertia weight initial Kennedy mathematical memetic methods multiobjective multiobjective optimization mutation Nash equilibria neighborhood neural networks noise nonlinear number of function number of iterations objective function optimization algorithms optimization problems overall best position parameter setting Pareto front Pareto optimal Parsopoulos & Vrahatis Parsopoulos and Vrahatis particle swarm optimization performance periodic orbits Petalas procedure PSO variants PSO[co PSO[in random reported in Table Rosenbrock function scheme search space selection simulation Skokos solutions standard PSO Step stochastic subswarms SUCCESS=1 swarm intelligence techniques test problems unification factor update UPSO vector velocity