In this paper, several interactive procedures for solving multiple criteria nonlinear programming problems have been developed. These are based on the generalized reduced gradient method for solving single objective n...
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In this paper, several interactive procedures for solving multiple criteria nonlinear programming problems have been developed. These are based on the generalized reduced gradient method for solving single objective nonlinear programming problems. They can handle maximization problems with nonlinear concave objectives, nonlinear convex constraints and an implicit quasi-concave preference function of the decision maker. The interactive procedures work with information of varying degrees of accuracy from the decision maker, thereby extending and strengthening a number of existing methods. [ABSTRACT FROM AUTHOR]
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them...
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We consider interactive algorithms in the pool-based setting, and in the stream-based setting. interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool-based algorithms can select elements at any order, while stream-based algorithms observe elements in sequence, and can only select elements immediately after observing them. We further consider an intermediate setting, which we term precognitive stream, in which the algorithm knows in advance the identity of all the elements in the sequence, but can select them only in the order of their appearance. For all settings, we assume that the suggested elements are generated independently from some source distribution, and ask what is the stream size required for emulating a pool algorithm with a given pool size, in the stream-based setting and in the precognitive stream setting. We provide algorithms and matching lower bounds for general pool algorithms, and for utility-based pool algorithms. We further derive nearly matching upper and lower bounds on the gap between the two settings for the special case of active learning for binary classification.
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them...
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We consider interactive algorithms in the pool-based setting, and in the stream-based setting. interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool-based algorithms can select elements at any order, while stream-based algorithms observe elements in sequence, and can only select elements immediately after observing them. We further consider an intermediate setting, which we term precognitive stream, in which the algorithm knows in advance the identity of all the elements in the sequence, but can select them only in the order of their appearance. For all settings, we assume that the suggested elements are generated independently from some source distribution, and ask what is the stream size required for emulating a pool algorithm with a given pool size, in the stream-based setting and in the precognitive stream setting. We provide algorithms and matching lower bounds for general pool algorithms, and for utility-based pool algorithms. We further derive nearly matching upper and lower bounds on the gap between the two settings for the special case of active learning for binary classification.
Damage detection in civil engineering is crucial for ensuring the safety and maintenance of infrastructure. This study introduces the development and implementation of innovative algorithms based on the Wavelet Transf...
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In this paper we show how to incorporate efficient feasible directions into interactive line search algorithms for multiple objective linear programming problems. The resulting new line search is guaranteed to generat...
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In this paper we show how to incorporate efficient feasible directions into interactive line search algorithms for multiple objective linear programming problems. The resulting new line search is guaranteed to generate points that lie in the efficient set. In addition, the new line search automatically corrects possible errors in judgement that the decision maker may inadvertently commit in giving some of his responses. These two improvements are achieved without sacrificing the key property of typical interactive line search algorithms, namely that they yield a new point at each iteration more preferred by the decision maker than the current point. The only additional computational requirement of the new line search is the solution of a single linear programming problem at each iteration. For these reasons, we advocate the incorporation of efficient feasible directions into interactive line search algorithms for multiple objective linear programs in the manner shown in this paper.
The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictio...
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The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions.
The purpose of this paper is to develop an interactive system for supporting the decision making process under multiple objectives and to empirically evaluate its performance. An interactive algorithm underlying the s...
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The purpose of this paper is to develop an interactive system for supporting the decision making process under multiple objectives and to empirically evaluate its performance. An interactive algorithm underlying the system is proposed with emphasis on the psychological aspects of the decision maker (DM). A choice process model is developed, based on pairwise comparison judgments of alternatives, because the judgments are basic and easy for a DM. A corresponding interactive algorithm is implemented and compared with other existing algorithms. Two kinds of comparative experiments, numerical and subject experiments, are conducted to verify the validity of the choice model as well as the practical effectiveness and the convergence of the algorithm.
Optimization algorithms or heuristics in which the user interacts significantly either during the solution process or as part of post-optimality analysis are becoming increasingly popular. An important underlying prem...
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We discuss the use of a quadratic norm for departures from the bliss value of a decision problem under conflicting objectives. The use of a quadratic norm is, for example, of interest within the dynamic framework of o...
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We propose a novel robust indicator-based algorithm, called IEMO/I, for interactive evolutionary multiple objective optimization. During the optimization run, IEMO/I selects at regular intervals a pair of solutions fr...
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ISBN:
(纸本)9781450361118
We propose a novel robust indicator-based algorithm, called IEMO/I, for interactive evolutionary multiple objective optimization. During the optimization run, IEMO/I selects at regular intervals a pair of solutions from the current population to be compared by the Decision Maker. The successively provided holistic judgements are employed to divide the population into fronts of potential optimality. These fronts are, in turn, used to bias the evolutionary search toward a subset of Pareto-optimal solutions being most relevant to the Decision Maker. To ensure a fine approximation of such a subset, IEMO/I employs a hypervolume metric within a steady-state indicator-based evolutionary framework. The extensive experimental evaluation involving a number of benchmark problems confirms that IEMO/I is able to construct solutions being highly preferred by the Decision Maker after a reasonable number of interactions. We also compare IEMO/I with some selected state-of-the-art interactive evolutionary hybrids incorporating preference information in form of pairwise comparisons, proving its competitiveness.
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