Author(s)

Amir Mosavi, Atieh Vaezipour

Faculty of Informatics, University of Debrecen, Debrecen, Hungary.
J?nk?ping University, J?nk?ping, Sweden.

During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the com-plete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.

Stochastic Local Search; Real-Life Application; Multi Criteria Decision Making; Multiobjective Optimization; Reactive Search Optimization

A. Mosavi and A. Vaezipour, "Reactive Search Optimization; Application to Multiobjective Optimization Problems," Applied Mathematics, Vol. 3 No. 10A, 2012, pp. 1572-1582. doi: 10.4236/am.2012.330217.