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Engineering optimization problems can be always classified into two main categories including the linear programming (LP) and nonlinear programming (NLP) problems. Each programming problem further involves the unconstrained conditions and constrained conditions for design variables of the optimized system. This paper will focus on the issue about the design problem of NLP with the constrained conditions. The employed method for such NLP problems is a variant of particle swarm optimization (PSO), named improved particle swarm optimization (IPSO). The developed IPSO is to modify the velocity updating formula of the algorithm to enhance the search ability for given optimization problems. In this work, many different kinds of physical engineering optimization problems are examined and solved via the proposed IPSO algorithm. Simulation results compared with various optimization methods reported in the literature will show the effectiveness and feasibility for solving NLP problems with the constrained conditions. [ABSTRACT FROM AUTHOR]

(Co,Mn) modified Gamma-Fe2O3 thin films with an in-plane coercivity of 2200 to 4000 Oe, a saturation magnetization of 250 to 310 emu/cm cubed, and a squareness ratio of 0.6 to 0.83 were prepared by DC-reactive magnetron sputtering. The films were deposited onto Si(100) and Corning #7059 glass substrates in an O2/Ar atmosphere by using alloy composite targets. Composition adjustment, reduction-oxidation and post-annealing treatments were found to affect coercivity as well as other magnetic properties in different extents. Owing to their sound magnetic performance, these films are promising for applications in high density magnetic recording.