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Summary form only given. Because of its ability to provide a bridge between linguistic expression and mathematical modeling fuzzy sets technology provides an idea framework for the construction of multi-criteria decision functions. In this talk we shall describe a number of aggregation operators associated with fuzzy set theory and see how they can be used to formulate multi-criteria decision functions...
Rational decision-making is often modeled as choosing the alternative that maximizes utility for the decision maker. Over the last few decades, much evidence has been produced to demonstrate that human decision-making is subject to irrationalities, such as intransitivity and framing biases. I seek an explanation for how these irrationalities arise, specifically, how they relate to the intrinsic nature...
We present a conceptual framework of an interactive method for solving integer linear vector optimization problems. The method is based on an enumerative cut approach. It combines cutting planes with enumerative parts. In this method the user can perform a structured searching process in the non-dominated set.
This paper introduces an effective computational environment for multi-objective decision-making, optimization and identification. The paper adopts multi-objective vector identification methodology and performance assessment provided by the Parameter Space Investigation method (PSI). The main feature of this methodology is in the fact that various design objectives are taken into consideration in...
This paper investigates the convergence paths, rate of convergence and the convergence half-space associated with a class of descent multi-objective optimization algorithms. The first order descent algorithms are defined by maximizing the local objectives' reductions which can be interpreted in either the primal space (parameters) or the dual space (objectives). It is shown that the convergence paths...
This paper considers a two-level linear programming problem involving random variable coefficients to cope with hierarchical decision making problems under uncertainty. Two decision making models are provided to optimize the mean of the objective function value or to minimize the variance. It is shown that the original problem is transformed into a deterministic problem. The computational methods...
If preferential independence is assumed inappro-priately when developing multicriterion search methods, biased results may occur. A new axiomatic approach to defining conditional preference orderings that naturally accounts for preferential dependencies is presented and illustrated. This approach applies both to scalar optimization techniques that identify a best solution and to evolutionary optimization...
Since Pareto optimal solutions in multi-objective optimization are not unique but makes a set, decision maker (DM) needs to select one of them as a final decision. In this event, DM tries to find a solution making a well balance among multiple objectives. Aspiration level methods support DM to do this in an interactive way, and are very simple, easy and intuitive for DMs. Their effectiveness has been...
Risk assessment is a common task present in a variety of problem domains, ranging from the assignment of premium classes to insurance applications, to the evaluation of disease treatments in medical diagnostics, situation assessments in battlefield management, state evaluations in planning activities, etc. Risk assessment involves scoring alternatives based on their likelihood to produce better or...
A new approach is proposed for the fuzzy multiple attribute decision making (MADM) problems with preference information on alternatives. In the approach, multiple decision makers give their preference information on alternatives in different formats. The uniformities and aggregation process with fuzzy majority method are employed to obtain the social fuzzy preference relation on the alternatives....
This paper presents a multiobjective genetic algorithm for obtaining fuzzy rules for subgroup discovery. This kind of fuzzy rules lets us represent knowledge about patterns of interest in an explanatory and understandable form which can be used by the expert. The multiobjective algorithm proposed in this paper defines three objectives. One of them is used as a restriction on the rules in order to...
In this paper we deal with multiobjective linear and quadratic programming problem with uncertain information. so far in the field of statistical analysis and data mining, e.g., mean-variance portfolio problem, support vector machine and their varieties, we have encountered various kinds of quadratic and linear programming problems with multiple criteria. Moreover coefficients in such problems have...
This paper presents a new multi-objective evolutionary algorithm (MOEA) which adopts a radial basis function (RBF) approach in order to reduce the number of fitness function evaluations performed to reach the Pareto front. The specific method adopted is derived from a comparative study conducted among several RBFs. In all cases, the NSGA-II (which is an approach representative of the state-of-the-art...
We describe a mechanism for optimal strategy generation from a Bayesian Belief Network (BBN). This system takes a BBN model either created by the user or derived from data. The user then specifies a set of goals (consisting of both objectives and constraints) and the observed and actionable variables in the model. The system then applies an optimizer to develop strategies that optimally achieve the...
In this paper, nonlinear dynamic system identification by using multiobjectively selected RBF network is considered. RBF networks are widely used as a model structure for nonlinear systems. The determination of its structure that is the number of basis functions is prior important step in system identification, and the tradeoff between model complexity and accuracy exists in this problem. By using...
In this paper, we focus on two-level integer programming problems with random variable coefficients in objective functions and/or constraints. Using chance constrained programming approaches in stochastic programming, the stochastic two-level integer programming problems are transformed into deterministic two-level integer programming problems. After introducing fuzzy goals for objective functions,...
Particle swarm optimization (PSO) was proposed by Kennedy et al. as a general approximate solution method for nonlinear programming problems. Its efficiency has been shown, but there have been left some shortcomings of the method. Thus, the authors proposed a revised PSO (rPSO) method incorporating the homomorphous mapping and the multiple stretching in order to cope with these shortcomings. In this...
In this paper, a new methodology is presented to solve multi-objective system redundancy allocation problems. A tabu search meta-heuristic approach is used to initially find the entire Pareto set, and then a Monte-Carlo simulation provides a decision maker with a pruned set of Pareto solutions based on decision maker's predefined objective function preferences. We are aiming to create a bridge between...
One of the basic engineering optimization problems is the problem of improving a prototype. This problem is constantly encountered by industrial and academic organizations that produce and design various objects (e.g., motor vehicles, machine tools, ships, and aircraft). This paper presents an approach for improving a prototype by construction of the feasible and Pareto sets while performing multicriteria...
The following topics are dealt with: computational intelligence; multicriteria decision making; mathematical programming; fuzzy set theory; and operations research
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