Reactive Search Optimization; Application to Multiobjective Optimization Problems

Mosavi, Amir; Vaezipour, Atieh

doi:10.4236/am.2012.330217

Cited by 28 publications

(16 citation statements)

References 31 publications

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“…Therefor the DM would deal with the diversity of the problems, stochasticity, and dynamicity more efficiently. Here are some case studies treated very promising by RSO [26,27,28,29,36]. Worth mentioning that RSO approach of learning on the job is contrasted with off-line accurate parameter tuning [39,40] which automatically tunes the parameter values of a stochastic local search algorithm.…”

Section: Rso and Visualization Tools; An Effective Approach To Mcdmmentioning

confidence: 99%

“…This has been the reason why EMO researchers were looking to find ways to efficiently integrate both optimization and decision making tasks in a convenient way [9,14,17,23,60] where the efficient MOO algorithms facilitate the DMs to consider multiple and conflicting goals of a MCDM problem simultaneously. Some examples of such algorithms and potential applications could be found in [25,26,27,28,29,30,37,59]. Nevertheless within the known approaches to solving complicated MCDM problems there are different ideologies and considerations in which any decisionmaking task would find a fine balance among them.…”

Section: Introductionmentioning

confidence: 99%

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A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Mosavi¹

2013

IJCA

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A novel environment for optimization, analytics and decision support in general engineering design problems is introduced. The utilized methodology is based on reactive search optimization (RSO) procedure and its recently implemented visualization software packages. The new set of powerful integrated data mining, modeling, visualiztion and learning tools via a handy procedure stretches beyond a decisionmaking task and attempts to discover new optimal designs relating to decision variables and objectives, so that a deeper understanding of the underlying problem can be obtained. In an optimal engineering design environment as such solving the multicriteria decision-making (MCDM) problem is considered as a combined task of optimization and decisionmaking. Yet in solving real-life MCDM problems often most of attention has been on finding the complete Pareto-optimal set of the associated multiobjective optimization (MOO) problem and less on decision-making. In this paper, along with presenting two case studies, the proposed interactive procedure which involves the decision-maker (DM) in the process addresses this issue effectively. Moreover the methodology delivers the capablity of handling the big data often associated with production decision-making as well as materials selection tasks in engineering design problems.

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Section: Rso and Visualization Tools; An Effective Approach To Mcdmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Mosavi¹

2013

IJCA

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“…In this framework the application of learning and intelligent optimization and reactive business intelligence approaches in improving the process of such complex optimization problems are described. Furthermore the problem is further reconsidered by reducing the dimensionality and the dataset size [12], multi-dimensional scaling, clustering and visualization tools [21][22][23][24][25][26][27][28].…”

Section: The Proposed and Former Approachesmentioning

confidence: 99%

Decision-Making in Complicated Geometrical Problems

Mosavi¹

2014

IJCA

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Due to increasing the number of decision-making criteria in today's ever complicated geometrical optimization problems, the traditional multiobjective optimization approaches, whether a priori, a posteriori or interactive's, found to be insufficient and ineffective. In this paper the drawbacks of the current algorithms are reviewed and the urgent need for inserting a learning component in the optimization loop is discussed. In the following the methodology of reactive optimization for evolutionary interactive multiobjective optimization for solving complicated geometrical decisionmaking problems is adopted. The proposed brain-computer optimization follows to the paradigm of learning while optimizing, through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme. At the end the effectiveness of the approach to geometrical problems is emphasized by providing the study case of optimal design problem of curves and surfaces.

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“…Rangavajhala et al (2006;Chen et al, 2012) suggested on Robust Design Optimization (RDO) and a new efficient sequential approximate MultiObjective Optimization (MOO) method for by obtaining the Pareto-optimal points respectively. Mosavi and Vaezipour (2012) developed a method on the basis of Reactive Search Optimization (RSO) algorithms in solving engineering optimal design and compared this method with Interactive Multi-objective Optimization and Decision-making method.…”

Section: Introductionmentioning

confidence: 99%

“…The multi-objective optimization formulation is carried out by employing the ENSES to these objective functions. The idea of this work is motivated from the studies of Mosavi and Vaezipour (2012;Steeves and Fleck, 2004a;Garcia, 2011). The rest of the paper is organised as follows.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Evolutionary Optimization of Sandwich Structures: An Evaluation by Elitist Non-dominated Sorting Evolution Strategy

Akmar¹,

Krämer²,

Rabczuk³

2015

American Journal of Engineering and Applied Sciences

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Abstract:In this study, an application of evolutionary multi-objective optimization algorithms on the optimization of sandwich structures is presented. The solution strategy is known as Elitist Non-Dominated Sorting Evolution Strategy (ENSES) wherein Evolution Strategies (ES) as Evolutionary Algorithm (EA) in the elitist Non-dominated Sorting Genetic algorithm (NSGA-II) procedure. Evolutionary algorithm seems a compatible approach to resolve multi-objective optimization problems because it is inspired by natural evolution, which closely linked to Artificial Intelligence (AI) techniques and elitism has shown an important factor for improving evolutionary multi-objective search. In order to evaluate the notion of performance by ENSES, the well-known study case of sandwich structures are reconsidered. For Case 1, the goals of the multi-objective optimization are minimization of the deflection and the weight of the sandwich structures. The length, the core and skin thicknesses are the design variables of Case 1. For Case 2, the objective functions are the fabrication cost, the beam weight and the end deflection of the sandwich structures. There are four design variables i.e., the weld height, the weld length, the beam depth and the beam width in Case 2. Numerical results are presented in terms of Paretooptimal solutions for both evaluated cases.

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Reactive Search Optimization; Application to Multiobjective Optimization Problems

Cited by 28 publications

References 31 publications

A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

A Multicriteria Decision Making Environment for Engineering Design and Production Decision-Making

Decision-Making in Complicated Geometrical Problems

Multi-Objective Evolutionary Optimization of Sandwich Structures: An Evaluation by Elitist Non-dominated Sorting Evolution Strategy

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