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Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 1999
"... Evolutionary algorithms (EA’s) are often wellsuited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a singl ..."
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Cited by 781 (22 self)
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Evolutionary algorithms (EA’s) are often wellsuited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EA’s are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the Strength Pareto EA (SPEA), that combines several features of previous multiobjective EA’s in a unique manner. It is characterized by a) storing nondominated solutions externally in a second, continuously updated population, b) evaluating an individual’s fitness dependent on the number of external nondominated points that dominate it, c) preserving population diversity using the Pareto dominance relationship, and d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proofofprinciple results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware–software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Paretooptimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EA’s on the 0/1 knapsack problem.
QoSaware middleware for web services composition
 IEEE TRANS. SOFTWARE ENG
, 2004
"... The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online BusinesstoBusiness (B2B) collaborations. In particular, the creation of valueadded services by composition of ..."
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Cited by 474 (6 self)
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The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online BusinesstoBusiness (B2B) collaborations. In particular, the creation of valueadded services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (tasklevel) selection of services and the other based on global allocation of tasks to services using integer programming.
Evolutionary Algorithms for Multiobjective Optimization
, 2002
"... Multiple, often conflicting objectives arise naturally in most realworld optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolutionbased methods have been used for multiobjective optimization for more than ..."
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Cited by 436 (14 self)
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Multiple, often conflicting objectives arise naturally in most realworld optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolutionbased methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.
Quality Driven Web Services Composition
, 2003
"... The processdriven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. ..."
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Cited by 222 (7 self)
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The processdriven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at designtime. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.
Multiobjective Optimization Using Evolutionary Algorithms  A Comparative Case Study
, 1998
"... . Since 1985 various evolutionary approaches to multiobjective optimization have been developed, capable of searching for multiple solutions concurrently in a single run. But the few comparative studies of different methods available to date are mostly qualitative and restricted to two approaches. I ..."
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Cited by 222 (12 self)
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. Since 1985 various evolutionary approaches to multiobjective optimization have been developed, capable of searching for multiple solutions concurrently in a single run. But the few comparative studies of different methods available to date are mostly qualitative and restricted to two approaches. In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem. 1 Introduction Many realworld problems involve simultaneous optimization of several incommensurable and often competing objectives. Usually, there is no single optimal solution, but rather a set of alternative solutions. These solutions are optimal in the wider sense that no other solutions in the search space are superior to them when all objectives are considered. They are known as Paretooptimal solutions. Mathematically, the concept of Paretooptimality can be defined as follows: Let us consider, without loss of generality, a multio...
Evaluating Evolutionary Algorithms
 Artificial Intelligence
, 1996
"... Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing se ..."
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Cited by 119 (14 self)
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Test functions are commonly used to evaluate the effectiveness of different search algorithms. However, the results of evaluation are as dependent on the test problems as they are on the algorithms that are the subject of comparison. Unfortunately, developing a test suite for evaluating competing search algorithms is difficult without clearly defined evaluation goals. In this paper we discuss some basic principles that can be used to develop test suites and we examine the role of test suites as they have been used to evaluate evolutionary search algorithms. Current test suites include functions that are easily solved by simple search methods such as greedy hillclimbers. Some test functions also have undesirable characteristics that are exaggerated as the dimensionality of the search space is increased. New methods are examined for constructing functions with different degrees of nonlinearity, where the interactions and the cost of evaluation scale with respect to the dimensionality of...
A PTAS for the Multiple Knapsack Problem
, 1993
"... The Multiple Knapsackproblem (MKP) is a natural and well known generalization of the single knapsack problem and is defined as follows. We are given a set of n items and m bins (knapsacks) such that each item i has a profit p(i) and a size s(i), and each bin j has a capacity c(j). The goal is to fin ..."
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Cited by 112 (2 self)
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The Multiple Knapsackproblem (MKP) is a natural and well known generalization of the single knapsack problem and is defined as follows. We are given a set of n items and m bins (knapsacks) such that each item i has a profit p(i) and a size s(i), and each bin j has a capacity c(j). The goal is to find a subset of items of maximum profit such that they have a feasible packing in the bins. MKP is a special case of the Generalized Assignment problem (GAP) where the profit and the size of an item can vary based on the specific bin that it is assigned to. GAP is APXhard and a 2approximation for it is implicit in the work of Shmoys and Tardos [26], and thus far, this was also the best known approximation for MKP. The main result of this paper is a polynomial time approximation scheme for MKP. Apart from its inherent theoretical interest as a common generalization of the wellstudied knapsack and bin packing problems, it appears to be the strongest special case of GAP that is not APXhard. We substantiate this by showing that slight generalizations of MKP that are very restricted versions of GAP are APXhard. Thus our results help demarcate the boundary at which instances of GAP becomeAPXhard. An interesting and novel aspect of our approach is an approximation preserving reduction from an arbitrary instance of MKP to an instance with O(log n) distinct sizes and profits.
Opportunistic Fair Scheduling over Multiple Wireless Channels
, 2003
"... Emerging spread spectrum highspeed data networks utilize multiple channels via orthogonal codes or frequencyhopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic chan ..."
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Cited by 109 (4 self)
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Emerging spread spectrum highspeed data networks utilize multiple channels via orthogonal codes or frequencyhopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a controlupdate problem that ensures longterm deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives.
On Quality of Service Optimization with Discrete QoS Options
 In Proceedings of the IEEE Realtime Technology and Applications Symposium
, 1999
"... Quality of Service (QoS) control is considered an important user demand and therefore receives wide attention, especially in the areas of computer networks and realtime multimedia systems. In this paper we present an QoS management scheme that enables us to quantitatively measure QoS, and to analyt ..."
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Cited by 94 (4 self)
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Quality of Service (QoS) control is considered an important user demand and therefore receives wide attention, especially in the areas of computer networks and realtime multimedia systems. In this paper we present an QoS management scheme that enables us to quantitatively measure QoS, and to analytically plan and allocate resource. In this model, available system resources are apportioned across multiple applications such that the net utility that accrues to the endusers of those applications is maximized. In [26, 27], we primarily work with “continuous ” QoS dimensions, and assumed that the ’utility ’ gained by improvements along a QoS dimension were always representable by concave functions. In this paper, we relax both assumptions. One, we deal with discrete set of QoS operating points. Two, we make no assumptions about the concavity of the utility functions. Using these as the basis, we tackle the problem of maximizing system utility by allocating a single finite resource to satisfy the QoS requirements of multiple applications along multiple QoS dimensions. We present two nearoptimal algorithms to solve this problem. The first yields an allocation within a known bounded distance from the optimal solution, and the second yields an allocation whose distance from the optimal solution can be explicitly controlled by the QoS manager. We compare the runtimes of these nearoptimal algorithms and their solution quality relative to the optimal allocation, which in turn is computed using dynamic programming. These detailed evaluations provide practical insight into which of these algorithms can be used online in realtime systems.
Facility location models for distribution system design
, 2004
"... The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamenta ..."
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Cited by 70 (0 self)
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The design of the distribution system is a strategic issue for almost every company. The problem of locating facilities and allocating customers covers the core topics of distribution system design. Model formulations and solution algorithms which address the issue vary widely in terms of fundamental assumptions, mathematical complexity and computational performance. This paper reviews some of the contributions to the current stateoftheart. In particular, continuous location models, network location models, mixedinteger programming models, and applications are summarized.