共查询到20条相似文献,搜索用时 46 毫秒
1.
Hisao Ishibuchi Kaname NarukawaNoritaka Tsukamoto Yusuke Nojima 《European Journal of Operational Research》2008
We have already proposed a similarity-based mating scheme to recombine extreme and similar parents for evolutionary multiobjective optimization. In this paper, we examine the effect of the similarity-based mating scheme on the performance of evolutionary multiobjective optimization (EMO) algorithms. First we examine which is better between recombining similar or dissimilar parents. Next we examine the effect of biasing selection probabilities toward extreme solutions that are dissimilar from other solutions in each population. Then we examine the effect of dynamically changing the strength of this bias during the execution of EMO algorithms. Computational experiments are performed on a wide variety of test problems for multiobjective combinatorial optimization. Experimental results show that the performance of EMO algorithms can be improved by the similarity-based mating scheme for many test problems. 相似文献
2.
《Optimization》2012,61(10):1661-1686
ABSTRACTOptimization over the efficient set of a multi-objective optimization problem is a mathematical model for the problem of selecting a most preferred solution that arises in multiple criteria decision-making to account for trade-offs between objectives within the set of efficient solutions. In this paper, we consider a particular case of this problem, namely that of optimizing a linear function over the image of the efficient set in objective space of a convex multi-objective optimization problem. We present both primal and dual algorithms for this task. The algorithms are based on recent algorithms for solving convex multi-objective optimization problems in objective space with suitable modifications to exploit specific properties of the problem of optimization over the efficient set. We first present the algorithms for the case that the underlying problem is a multi-objective linear programme. We then extend them to be able to solve problems with an underlying convex multi-objective optimization problem. We compare the new algorithms with several state of the art algorithms from the literature on a set of randomly generated instances to demonstrate that they are considerably faster than the competitors. 相似文献
3.
Recently, a general-purpose local-search heuristic method called extremal optimization (EO) has been successfully applied to some NP-hard combinatorial optimization problems. This paper presents an investigation on EO with its application in numerical multiobjective optimization and proposes a new novel elitist (1 + λ) multiobjective algorithm, called multiobjective extremal optimization (MOEO). In order to extend EO to solve the multiobjective optimization problems, the Pareto dominance strategy is introduced to the fitness assignment of the proposed approach. We also present a new hybrid mutation operator that enhances the exploratory capabilities of our algorithm. The proposed approach is validated using five popular benchmark functions. The simulation results indicate that the proposed approach is highly competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOEO can be considered a good alternative to solve numerical multiobjective optimization problems. 相似文献
4.
Kazhal Khaledian 《Numerical Functional Analysis & Optimization》2013,34(11):1431-1447
In this article, efficient solutions of multiple objective optimization problems with trade-offs, bounded by prespecified lower and upper bounds, are investigated. It is shown that one of the existing techniques to obtain these solutions does not work correctly. Furthermore, a new approach is given to approximate the set of such solutions. In addition to theoretical discussions, some clarifying examples are given. 相似文献
5.
In this article, a new framework for evolutionary algorithms for approximating the efficient set of a multiobjective optimization (MOO) problem with continuous variables is presented. The algorithm is based on populations of variable size and exploits new elite preserving rules for selecting alternatives generated by mutation and recombination. Together with additional assumptions on the considered MOO problem and further specifications on the algorithm, theoretical results on the approximation quality such as convergence in probability and almost sure convergence are derived. 相似文献
6.
TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study
Srinivas Soumitri Miriyala Venkat R. Subramanian Kishalay Mitra 《European Journal of Operational Research》2018,264(1):294-309
Artificial Neural Networks (ANNs) are well known for their credible ability to capture non-linear trends in scientific data. However, the heuristic nature of estimation of parameters associated with ANNs has prevented their evolution into efficient surrogate models. Further, the dearth of optimal training size estimation algorithms for the data greedy ANNs resulted in their overfitting. Therefore, through this work, we aim to contribute a novel ANN building algorithm called TRANSFORM aimed at simultaneous and optimal estimation of ANN architecture, training size and transfer function. TRANSFORM is integrated with three standalone Sobol sampling based training size determination algorithms which incorporate the concepts of hypercube sampling and optimal space filling. TRANSFORM was used to construct ANN surrogates for a highly non-linear industrially validated continuous casting model from steel plant. Multiobjective optimization of casting model to ensure maximum productivity, maximum energy saving and minimum operational cost was performed by ANN assisted Non-dominated Sorting Genetic Algorithms (NSGA-II). The surrogate assisted optimization was found to be 13 times faster than conventional optimization, leading to its online implementation. Simple operator's rules were deciphered from the optimal solutions using Pareto front characterization and K-means clustering for optimal functioning of casting plant. Comprehensive studies on (a) computational time comparisons between proposed training size estimation algorithms and (b) predictability comparisons between constructed ANNs and state of art statistical models, Kriging Interpolators adds to the other highlights of this work. TRANSFORM takes physics based model as the only input and provides parsimonious ANNs as outputs, making it generic across all scientific domains. 相似文献
7.
In solving multi-objective optimization problems, evolutionary algorithms have been adequately applied to demonstrate that multiple and well-spread Pareto-optimal solutions can be found in a single simulation run. In this paper, we discuss and put together various different classical generating methods which are either quite well-known or are in oblivion due to publication in less accessible journals and some of which were even suggested before the inception of evolutionary methodologies. These generating methods specialize either in finding multiple Pareto-optimal solutions in a single simulation run or specialize in maintaining a good diversity by systematically solving a number of scalarizing problems. Most classical generating methodologies are classified into four groups mainly based on their working principles and one representative method from each group is chosen in the present study for a detailed discussion and for its performance comparison with a state-of-the-art evolutionary method. On visual comparisons of the efficient frontiers obtained for a number of two and three-objective test problems, the results bring out interesting insights about the strengths and weaknesses of these approaches. The results should motivate researchers to design hybrid multi-objective optimization algorithms which may be better than each of the individual methods. 相似文献
8.
We apply Algorithm Robust to various problems in multiple objective discrete optimization. Algorithm Robust is a general procedure
that is designed to solve bicriteria optimization problems. The algorithm performs a weight space search in which the weights
are utilized in min-max type subproblems. In this paper, we experiment with Algorithm Robust on the bicriteria knapsack problem,
the bicriteria assignment problem, and the bicriteria minimum cost network flow problem. We look at a heuristic variation
that is based on controlling the weight space search and has an indirect control on the sample of efficient solutions generated.
We then study another heuristic variation which generates samples of the efficient set with quality guarantees. We report
results of computational experiments. 相似文献
9.
Ignacy Kaliszewski Janusz MiroforidisDmitry Podkopaev 《European Journal of Operational Research》2012,216(1):188-199
We present an approach to interactive Multiple Criteria Decision Making based on preference driven Evolutionary Multiobjective Optimization with controllable accuracy.The approach relies on formulae for lower and upper bounds on coordinates of the outcome of an arbitrary efficient variant corresponding to preference information expressed by the Decision Maker. In contrast to earlier works on that subject, here lower and upper bounds can be calculated and their accuracy controlled entirely within evolutionary computation framework. This is made possible by exploration of not only the region of feasible variants - a standard within evolutionary optimization, but also the region of infeasible variants, the latter to our best knowledge being a novel approach within Evolutionary Multiobjective Optimization.To illustrate how this concept can be applied to interactive Multiple Criteria Decision Making, two algorithms employing evolutionary computations are proposed and their usefulness demonstrated by a numerical example. 相似文献
10.
In this paper, the optimization of time-varying objective functions, known only through estimates, is considered. Recent research defined algorithms for static optimization problems. Based on one of these algorithms, we derive an optimization scheme for the time-varying case. In stochastic optimization problems, convergence of an algorithm to the optimum prevents the algorithm from being efficiently adaptive to changes of the objective function if it is time-varying. So, convergence cannot be required in a time-varying scenario. Rather, we require convergence to the optimum with high probability together with a satisfactory dynamical behavior. Analytical and simulative results illustrate the performance of the proposed algorithm compared with other optimization techniques. 相似文献
11.
This paper proposes a satisfying optimization method based on goal programming for fuzzy multiple objective optimization problem. The aim of this presented approach is to make the more important objective achieving the higher desirable satisfying degree. For different fuzzy relations and fuzzy importance, the reformulated optimization models based on goal programming is proposed. Not only the satisfying results of all the objectives can be acquired, but also the fuzzy importance requirement can be simultaneously actualized. The balance between optimization and relative importance is realized. We demonstrate the efficiency, flexibility and sensitivity of the proposed method by numerical examples. 相似文献
12.
An interactive satisficing method based on alternative tolerance is proposed for fuzzy multiple objective optimization. The new tolerances of the dissatisficing objectives are generated using an auxiliary programming problem. According to the alternative tolerant limits, either the membership functions are changed, or the objective constraints are added. The lexicographic two-phase programming is implemented to find the final solution. The results of the dissatisficing objectives are iteratively improved. The presented method not only acquires the efficient or weak efficient solution of all the objectives, but also satisfies the progressive preference of decision maker. Numerical examples show its power. 相似文献
13.
In practical applications of mathematical programming it is frequently observed that the decision maker prefers apparently
suboptimal solutions. A natural explanation for this phenomenon is that the applied mathematical model was not sufficiently
realistic and did not fully represent all the decision makers criteria and constraints. Since multicriteria optimization approaches
are specifically designed to incorporate such complex preference structures, they gain more and more importance in application
areas as, for example, engineering design and capital budgeting. The aim of this paper is to analyze optimization problems
both from a constrained programming and a multicriteria programming perspective. It is shown that both formulations share
important properties, and that many classical solution approaches have correspondences in the respective models. The analysis
naturally leads to a discussion of the applicability of some recent approximation techniques for multicriteria programming
problems for the approximation of optimal solutions and of Lagrange multipliers in convex constrained programming. Convergence
results are proven for convex and nonconvex problems. 相似文献
14.
Mario Villalobos-Arias Carlos A. Coello Coello Onésimo Hernández-Lerma 《Mathematical Methods of Operations Research》2006,64(2):353-362
In this paper we consider a simulated annealing algorithm for multiobjective optimization problems. With a suitable choice of the acceptance probabilities, the algorithm is shown to converge asymptotically, that is, the Markov chain that describes the algorithm converges with probability one to the Pareto optimal set. 相似文献
15.
The weighted sums approach for linear and convex multiple criteria optimization is well studied. The weights determine a linear function of the criteria approximating a decision makers overall utility. Any efficient solution may be found in this way. This is not the case for multiple criteria integer programming. However, in this case one may apply the more general e-constraint approach, resulting in particular single-criteria integer programming problems to generate efficient solutions. We show how this approach implies a more general, composite utility function of the criteria yielding a unified treatment of multiple criteria optimization with and without integrality constraints. Moreover, any efficient solution can be found using appropriate composite functions. The functions may be generated by the classical solution methods such as cutting plane and branch and bound algorithms. 相似文献
16.
A hybrid immune multiobjective optimization algorithm 总被引:1,自引:0,他引:1
In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently. 相似文献
17.
In this note we show that many classes of global optimization problems can be treated most satisfactorily by classical optimization theory and conventional algorithms. We focus on the class of problems involving the minimization of the product of several convex functions on a convex set which was studied recently by Kunoet al. [3]. It is shown that these problems are typical composite concave programming problems and thus can be handled elegantly by c-programming [4]–[8] and its techniques. 相似文献
18.
Tradeoff directions in multiobjective optimization problems 总被引:2,自引:0,他引:2
Multiobjective optimization problems (MOP) typically have conflicting objectives wherein a gain in one objective is at the
expense of another. Tradeoff directions, which measure the change in some objectives relative to changes in others, provide
important information as to the best direction of improvement from the current solution. In this paper we present a general
definition of tradeoffs as a cone of directions and provide a general method of calculating tradeoffs at every Pareto optimal
point of a convex MOP. This extends current definitions of tradeoffs which assume certain conditions on the feasible set and
the objective functions. Two comprehensive numerical examples are provided to illustrate the tradeoff directions and the methods
used to calculate them. 相似文献
19.
Horst W. Hamacher Christian Roed Pedersen Stefan Ruzika 《European Journal of Operational Research》2007
In this paper, theory and algorithms for solving the multiple objective minimum cost flow problem are reviewed. For both the continuous and integer case exact and approximation algorithms are presented. In addition, a section on compromise solutions summarizes corresponding results. The reference list consists of all papers known to the authors which deal with the multiple objective minimum cost flow problem. 相似文献
20.
Multi-objective optimization using evolutionary algorithms identifies Pareto-optimal alternatives or their close approximation by means of a sequence of successive local improvement moves. While several successful applications to combinatorial optimization problems are known, studies of underlying problem structures are still scarce. 相似文献