Research On Multi-Objective Particle Swarm Optimization Algorithms And Its Application In BOF Steelmaking Process

Many problems in scientific studies and engineering practices could be transformed into multi-objective problems. As one of the swarm intelligence based models, particle swarm optimization(PSO) is is easy to implement and has fast convergence speed. PSO is suitable for solving multi-objective problems and a great number of scholars have been researching multi-objective particle swarm optimization algorithms.The research of the multi-objective particle swarm optimization has achieved lots of results, but there are still some deficiencies to be improved. On the one hand, most multi-objective particle swarm optimization algorithms lack the mechanisms of monitoring the swarm evolutionary environment. These algorithms cannot obtain real-time information feedback and are hard to decide when to adjust which evolutionary strategy to what extent. On the other hand, when solving the many objective problems, the optimization performance of particle swarm optimization algorithms degrades severely. The main contents of this paper are as follows:(1). To deal with the lack of mechanisms of monitoring the evolutionary environment, we designed the mechanisms and proposed two improved multi-objective particle swarm optimization algorithms to balance the global and local researching abilities, the convergence and diversity respectively. In the adaptive multi-objective particle swarm optimization algorithm with gaussian chaotic mutation and elite learning, we adjust the inertia weight and acceleration coefficients adaptively by monitoring the convergence state of swarm. Further more, an elite learning strategy and an improved mutation operator with the features of Gaussian function and chaotic sequence are proposed to adjust the local and global researching abilities. In the adaptive multi-objective particle swarm optimization algorithm based on the status of external archive, we combined two global best selection strategies and adjust the execution probability of the two strategies adaptively by monitoring the state of external archive. Further more, perturbation operators are applied to the particles in swarm and solutions in archive, and the perturbation probabilities are adjusted adaptively by the status of external archive to balance the convergence and diversity.(2). We proposed a reference-point-based particle swarm optimization algorithm for many-objective optimization. Aiming at the insufficiency of performance when solving many-objective optimization problems, we introduced a structured set of reference points in the objective space. After that, we can select the solutions which have both good convergence and diversity as the global best. Further more, we proposed a truncation method of external archive based on the reference points to maintain the diversity of the solution set.(3). We applied our multi-objective particle swarm optimization algorithm to the calculation of adding alloy in basic oxygen furnace steelmaking process, how to reduce the costs while ensure the contents of the liquid steel meet the requirement is an important issue which affect the steel plant's benefits. On the basis of the soft measurement by echo state network, we transformed the the issue to solve a multi-objective optimization problem. After that, our improved multi-objective particle swarm optimization algorithm is applied to solve the problem. Simulation results on real data of basic oxygen furnace steelmaking showed that our method could reduce the costs of alloys and ensure the contents of the liquid steel meet the requirement.