Constrained non-linear optimization by modified particle swarm optimization

Abstract

This paper presents a modified particle swarm optimization (MPSO) for constrained non-linear optimization problems. Optimization problems are very complex in real life applications. The proposed modified PSO consists of problem (complexity) dependent variable number of promising values (in velocity vector), error-iteration dependent step length, unlocking the dead look of idle particles and so on. It reliably and accurately tracks a continuously changing solution of the complex function and no extra concentration/effort is needed for more complex higher order functions. Constraint management is incorporated in the modified PSO by penalty function. The modified PSO has balance between local and global searching abilities, and an appropriate fitness function helps to converge it quickly. To avoid the method to be frozen, stagnated/idle particles are reset. Finally, benchmark data and methods are used to show the effectiveness of the proposed method.