A polyhedral approach to solving multicriterion combinatorial optimization problems over sets of polyarrangements

Multicriterion discrete optimization problems over feasible combinatorial sets of polyarrangements are considered. Structural properties of feasible domains and different types of efficient solutions are investigated. Based on the ideas of Euclidean combinatorial optimization and the major criterion method, a polyhedral approach to the solution of the problems is developed and substantiated. Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 118-126, May-June 2009.     

Subclasses of solvable problems from classes of combinatorial optimization problems

Well-known subclasses of solvable problems from classes of combinatorial optimization are reviewed. For solvable problems such as the traveling salesman problem, location problem, assignment problem, and clustering problem, the changes in the objective function on a given ordering of combinatorial configurations are analyzed. Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 97–105, March–April 2009.     

A note on robustness tolerances for combinatorial optimization problems

We consider so-called generic combinatorial optimization problem, where the set of feasible solutions is some family of nonempty subsets of a finite ground set with specified positive initial weights of elements, and the objective function represents the total weight of elements of the feasible solution. We assume that the set of feasible solutions is fixed, but the weights of elements may be perturbed or are given with errors. All possible realizations of weights form the set of scenarios.A feasible solution, which for a given set of scenarios guarantees the minimum value of the worst-case relative regret among all the feasible solutions, is called a robust solution. The maximum percentage perturbation of a single weight, which does not destroy the robustness of a given solution, is called the robustness tolerance of this weight with respect to the solution considered.In this paper we give formulae for computing the robustness tolerances with respect to an optimal solution obtained for some initial weights and we show that this can be done in polynomial time whenever the optimization problem is polynomially solvable itself.     

An approach to solving discrete vector optimization problems over a combinatorial set of permutations

Complex discrete multicriteria problems over a combinatorial set of permutations are analyzed. Some properties of an admissible domain for a combinatorial multicriteria problem embedded into an arithmetic Euclidian space are considered. Optimality conditions are obtained for different types of effective solutions. A new approach to solving the problems formulated is constructed and substantiated. This work was supported by the Fundamental Research Fund of Ukraine (project Φ251/094). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 158–172, May–June 2008.     

On the computational complexity of combinatorial flexibility problems

The concept of flexibility – originated in the context of heat exchanger networks design – is associated with a substructure which allows the same optimal value on the substructure (for example an optimal flow) as in the whole structure, for all the costs in a given range of costs. In this work, we extend the concept of flexibility to general combinatorial optimization problems, and prove several computational complexity results in this new framework. Under some monotonicity conditions, we prove that a combinatorial optimization problem can be polynomially reduced to its associated flexibility problem. However, the minimum cut, maximum weighted matching and shortest path problems have NP-complete associated flexibility problems. In order to obtain polynomial flexibility problems, we have to restrict ourselves to combinatorial optimization problems on matroids.     

一类组合优化问题的混合进化算法

针对问题空间为全排列集合的一类组合优化问题,提出了一种混合进化算法。在自然进位制编码的基础上,算法采用了遗传算法的单点交叉算子和进化规划的高斯扰动算子,并运用了精英保留策略;算法实现时采用逐位运算法实现大数值运算,避免了运算溢出,减少了运算量。分析和模拟计算结果表明,新算法具有可行性、有效性和通用性。     

Linear time transformations between combinatorial problems

Besides showing the existence of NP-complete problems, Cook's theorem and subsequent developments have resulted in a large number of polynomial-time transformations between combinatorial problems. For both theoretical and practical reasons it is important to pursue these transformations, especially low-order ones. In this paper, two linear classes are displayed: it is shown that satisfiability, 3-colourability and set-splitting are linear-time equivalent. It is also shown that bipartite matching and path-connectivity are linear-time equivalent. The computational model on which these results are based is a 1-tape transducer.     

A novel differential evolution mapping technique for generic combinatorial optimization problems

Differential evolution is primarily designed and used to solve continuous optimization problems. Therefore, it has not been widely considered as applicable for real-world problems that are characterized by permutation-based combinatorial domains. Many algorithms for solving discrete problems using differential evolution have been proposed, some of which have achieved promising results. However, to enhance their performance, they require improvements in many aspects, such as their convergence speeds, computational times and capabilities to solve large discrete problems. In this paper, we present a new mapping method that may be used with differential evolution to solve combinatorial optimization problems. This paper focuses specifically on the mapping component and its effect on the performance of differential evolution. Our method maps continuous variables to discrete ones, while at the same time, it directs the discrete solutions produced towards optimality, by using the best solution in each generation as a guide. To judge its performance, its solutions for instances of well-known discrete problems, namely: 0/1 knapsack, traveling salesman and traveling thief problems, are compared with those obtained by 8 other state-of-the-art mapping techniques. To do this, all mapping techniques are used with the same differential evolution settings. The results demonstrated that our technique significantly outperforms the other mapping methods in terms of the average error from the best-known solution for the traveling salesman problems, and achieves promising results for both the 0/1 knapsack and the traveling thief problems.     

Interval-parameter optimization problems on graphs

Well-known optimization problems on graphs are considered under uncertainty when parameter domains are specified in the form of intervals. Exponential estimates of computational complexity of problems being studied and also problems that are polynomial in the classical formulation are substantiated. Polynomially solvable subclasses are found, and sufficient conditions of statistical efficiency of a proposed approximate algorithm are constructively substantiated. Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 3–14, March–April 2009.     

Vector optimization problems with linear criteria over a fuzzy combinatorial set of alternatives

Vector optimization problems over a fuzzy combinatorial set of permutations are investigated. Based on the properties of the convex hull of a fuzzy combinatorial set of permutations, modifications of multicriteria choice methods are developed and substantiated for a fuzzy feasible combinatorial set. Mathematical models of some application problems are presented.     

Multiobjective combinatorial optimization problems with a cost and several bottleneck objective functions: An algorithm with reoptimization

This paper addresses multicriteria combinatorial optimization problems involving one cost and several bottleneck objective functions. An algorithm is developed which generates the minimal complete set of Pareto-optimal solutions. This algorithm runs in polynomial time as long as the single objective problem considering only the cost function can be solved polynomially. A reoptimization procedure is used to accelerate the convergence of the algorithm. Applications are given. Computational results on randomly generated instances and planar grid graphs concerning the minimum cost spanning tree and the shortest path problem are presented.     

Algorithms for Solving Lexicographic Multicriterion Problems on Matroids

Algorithms for lexicographic multicriterion problems on matroids and multicriterion problems solved by the concession method are described. The algorithms are based on the polynomial procedure of finding a sequence of the best solutions to an extremum problem.     

New meta-heuristic for combinatorial optimization problems: Intersection based scaling

Combinatorial optimization problems are found in many application fields such as computer science,engineering and economy. In this paper, a new efficient meta-heuristic, Intersection-Based Scaling (IBS for abbreviation), is proposed and it can be applied to the combinatorial optimization problems. The main idea of IBS is to scale the size of the instance based on the intersection of some local optima, and to simplify the search space by extracting the intersection from the instance, which makes the search more efficient. The combination of IBS with some local search heuristics of different combinatorial optimization problems such as Traveling Salesman Problem (TSP) and Graph Partitioning Problem (GPP) is studied, and comparisons are made with some of the best heuristic algorithms and meta-heuristic algorithms. It is found that it has significantly improved the performance of existing local search heuristics and significantly outperforms the known best algorithms.     

Analysis of mathematical models and methods of solving combinatorial optimization problems on game-type permutations

The paper is concerned with an optimization problem on game-type permutations, where one or both players have combinatorial constraints on their strategies. A mathematical model of such problems is constructed and analyzed. A modified graphical method is proposed to solve (2xn)-and (mx2)-dimensional problems. High-dimensional problems are reduced to linear programming and combinatorial optimization problems. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 103–114, November–December 2007.     

Bid evaluation in combinatorial auctions: optimization and learning

In combinatorial auctions bidders can post bids on groups of items. The problem of selecting the winning bids, called Winner Determination Problem, is NP‐hard. In this paper, we consider an interesting variant of this problem, called Bid Evaluation Problem (BEP), where items are services and are subject to precedence constraints and temporal windows. The BEP has many practical applications, such as, for instance, in transportation routes auctions and in take off and landing time slot allocation problems. We have developed a number of optimization algorithms based on Constraint Programming, on Integer Programming and on the combination of the two techniques. We first show that all algorithms proposed outperform the only commercial system for solving BEP instances called Multi AGent Negotiation Testbed, a more general tool for agent negotiation. Then, we evaluate the developed algorithms and use the decision tree machine learning technique for finding a relation between the instance structure and the solving algorithm and providing an automatic algorithm selection procedure. We show that we can achieve an accuracy of 90% in predicting the best algorithm given the instance to be solved with a significant time saving w.r.t. a single solving technique or a costless, but less accurate, prediction technique. Copyright © 2009 John Wiley & Sons, Ltd.     

求解组合优化问题的鱼群算法的收敛性证明

为了证明求解组合优化问题的人工鱼群算法的全局收敛性,将人工鱼群算法的搜索空间定义为离散空间,其中的每个点即为一个人工鱼的位置状态,其食物浓度即为该点的目标函数值。根据食物浓度大小将整个离散空间集合分为若干个非空子集;将所有人工鱼集合也对应划分为若干个非空子集。在人工鱼的觅食、聚群和追尾过程中,人工鱼从一个位置状态转移到任意一个位置状态的转移概率可以计算出来;人工鱼移动过程中的每个位置状态对应于有限Markov链上的一个状态,且满足可归约随机矩阵的稳定性条件,据此证明了工鱼群算法具有全局收敛性。     

Multicriteria path and tree problems: discussion on exact algorithms and applications

Multicriteria/multiobjective path and tree models are useful in many applications. Particularly, in Internet routing problems they seem to lead to promising approaches. In the first part of this paper, we classify and present the main exact approaches dealing with several multicriteria path problems putting in evidence the shortest path problem. In the second part, we review exact algorithms dedicated to some multicriteria tree problems, namely the minimum spanning tree and the minimum cost/minimum label spanning tree problems. Finally, the application of these models is exemplified.     

蚁群优化算法在求解随机组合优化问题中的应用综述*

不确定条件下的优化问题更贴近真实世界环境,因而日益受到广泛关注。综述了蚁群优化在求解一组不确定条件下的组合优化问题,即随机组合优化问题方面的应用。首先介绍了不确定条件下组合优化问题的概念分类模型,给出了随机组合优化问题的一般定义;然后指出了其与求解传统确定性组合优化问题的不同之处,即目标函数的计算存在不确定性,并详细论述了目前解决方法的进展;最后分析了该领域值得重点关注的几个研究方向,并对其未来发展进行了展望。