Interactive particle swarm optimization

It is often desirable to simultaneously handle several objectives and constraints in practical optimization problems. In some cases, these objectives and constraints are non-commensurable and they are not explicitly/mathematically available. For this kind of problems, interactive optimization may be a good approach. Interactive optimization means that a human user evaluates the potential solutions in qualitative way. In recent years evolutionary computation (EC) was applied for interactive optimization, which approach has became known as interactive evolutionary computation (IEC). The aim of this paper is to propose a new interactive optimization method based on particle swarm optimization (PSO). PSO is a relatively new population based optimization approach, whose concept originates from the simulation of simplified social systems. The paper shows that interactive PSO cannot be based on the same concept as IEC because the information sharing mechanism of PSO significantly differs from EC. So this paper proposes an approach which considers the unique attributes of PSO. The proposed algorithm has been implemented in MATLAB (IPSO toolbox) and applied to a case-study of temperature profile design of a batch beer fermenter. The results show that IPSO is an efficient and comfortable interactive optimization algorithm. The developed IPSO toolbox (for Mat-lab) can be downloaded from the Web site of the authors: http://www.fmt.vein.hu/softcomp/ipso.