We consider a user communicating over a fading channel with perfect channel state information. Data is assumed to arrive from some higher layer application and is stored in a buffer until it is transmitted. We study adapting the user's transmission rate and power based on the channel state information as well as the buffer occupancy; the objectives are to regulate both the long-term average transmission power and the average buffer delay incurred by the traffic. Two models for this situation are discussed; one corresponding to fixed-length/variable-rate codewords and one corresponding to variable-length codewords. The trade-off between the average delay and the average transmission power required for reliable communication is analyzed. A dynamic programming formulation is given to find all Pareto optimal power/delay operating points. We then quantify the behavior of this tradeoff in the regime of asymptotically large delay. In this regime we characterize simple buffer control policies which exhibit optimal characteristics. Connections to the delay-limited capacity and the expected capacity of fading channels are also discussed.

Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives (i.e., attributes or criteria) have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The most recent development in the field of decision analysis has yielded a rigorous technique for combining attributes multiplicatively (thereby incorporating nonlinearity), and for handling uncertainty in the attribute values. But MultiAttribute Utility Analysis (MAUA) provides only a mapping from a vector-valued objective function to a scalar-valued function, and does not address the difficulty of searching large problem spaces. Genetic algorithms (GAs), on the other hand, are well suited to searching intractably large, poorly understood problem spaces, but have mostly been used to optimize a single objective. The direct combination of MAUA and GAs is a logical next step...

This work makes two contributions to geometric motion planning for multiple robots: i) Motion plans are computed that simultaneously optimize an independent performance measure for each robot; ii) A general spectrum is defined between decoupled and centralized planning, in which we introduce coordination along independent roadmaps. By considering independent performance measures, we introduce a form of optimality that is consistent with concepts from multi-objective optimization and game theory literature. Previous multiple-robot motion planning approaches that consider optimality combine individual performance measures into a scalar criterion. As a result, these methods can fail to find many potentially useful motion plans. We present implemented, multiple-robot motion planning algorithms that are derived from the principle of optimality, for three problem classes along the spectrum between centralized and decoupled planning: i) coordination along fixed, independent paths; ii) coordin...

Abstract not found

Evolutionary computation is a useful technique for learning behaviors in multiagent systems. Among the several types of evolutionary computation, one natural and popular method is to coevolve multiagent behaviors in multiple, cooperating populations. Recent research has suggested that coevolutionary systems may favor stability rather than performance in some domains. In order to improve upon existing methods, this paper examines the idea of modifying traditional coevolution, biasing it to search for maximal rewards. We introduce a theoretical justification of the improved method and present experiments in three problem domains. We conclude that biasing can help coevolution find better results in some multiagent problem domains. 1

This paper presents a decentralized method for computing Pareto-optimal solutions in multiparty negotiations over continuous issues. The method is based on the well known weighting method which is decomposed by introducing an own decision variable for each decision maker and by applying the dual decomposition method to the resulting problem. The method offers a systematic way for generating some or all Pareto-optimal solutions so that decision makers do not have to know each others' value functions. Under the assumption of quasilinear value function the requirement that a decision maker knows the explicit form for his value function can be relaxed. In that case the decision maker is asked to solve a series of multiobjective programming problems where an additional articial decision variable is introduced.

The concept of efficiency as it applies to Decision Making Units (DMUs), solutions, alternatives plays an important role both in Data Envelopment Analysis (DEA) and Multiple Objective Linear Programming (MOLP). Despite this and other apparent similarities, DEA and MOLP research has developed separately. We show that structurally the DEA formulation to identify efficient units is quite similar to the MOLP model based on the reference point or the reference direction approach to generate efficient solutions. DEA and MOLP should not be seen as substitutes, but rather as complements. We show that they cross-fertilize each other. MOLP provides interesting extensions to DEA and DEA provides new areas of application to MOLP. Keywords: Data Envelopment Analysis, Multiple Objective Linear Programming, Efficiency, Performance Measurement 1 1. Introduction Data Envelopment Analysis (DEA) was originally proposed by Charnes, Cooper and Rhodes [1978] as a method for evaluating the relative effici...

Abstract not found

In this paper we propose a new way to iteratively solve the image reconstruction problem from noisy images or noisy data linearly related to the pixel intensities. This is done by exploiting the relation between Tikhonov regularization and multiobjective optimization to obtain iteratively approximations to the Tikhonov L-curve and its corner. Monitoring the change of the approximate L-curves allows us to adjust the regularization parameter adaptively during a preconditioned conjugate gradient iteration, so that the desired image can be reconstructed with a low number of iterations. Nonnegativity constraints are taken into account automatically. We present test results on image reconstruction in positron emission tomography (PET). Keywords: Tikhonov regularization, multiobjective optimization, ill-posed, L-curve, envelope, preconditioned conjugate gradients, image reconstruction, positron emission tomography (PET) 1991 MSC Classification: primary 65F10, secondary 65R30, 68U10, 90C29, 9...

This paper proposes a cross-layer optimization framework that provides efficient allocation of wireless network resources across multiple types of applications to maximize network capacity and user satisfaction. We define a novel optimization scheme based on the Mean Opinion Score (MOS) as the unifying metric. Our experiments, applied to scenarios where users simultaneously run three types of applications, such as realtime voice, video conferencing and file download, confirm that MOS-based optimization leads to significant improvement in terms of user perceived quality when compared to throughput-based optimization.