Abstract

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.

Highlights

  • In the modern-day of optimal design and decision-making, optimization plays the main role [1,2]

  • In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered

  • In problem-solving methods of Stochastic Local Search, where the free parameters are tuned through a feedback loop, the user is considered as a crucial learning component in which different options are developed and tested until acceptable results are obtained

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Summary

Introduction

In the modern-day of optimal design and decision-making, optimization plays the main role [1,2]. Most studies in the past concentrated in finding the optimum corresponding to only a single design objective. In the real-life design problems there are numerous objectives to be considered at once. Efficient multiobjective optimization algorithms facilitate the decision makers to consider more than one conflicting goals simultaneously. Some examples of such algorithms and potantial applications could be found in [3,4,5,6,7]. Within the known approachs to solving complicated optimal design problems there are different ideologies and considerations in which any decision-making task would find a fine balance among them

Statement of the General form of the Multiobjective Optimization Problems
Combination of EMO and MCDM
Stochastic Local Search
From Local Search to RSO by Learning
Characteristics of the Proposed Approach
Applications
Software Architectur Packages for the Proposed Reactive and Interactive MCDM
Creating the Model with Scilab
Setting up the RSO Software
10. Conclusions

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