. This paper presents a critical review of the most important evolutionary-based multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms. Each technique is briefly described mentioning its advantages and disadvantages, their degree of applicability and some of their known applications. Finally, the future trends in this discipline and some of the open areas of research are also addressed. Keywords: multiobjective optimization, multicriteria optimization, vector optimization, genetic algorithms, evolutionary algorithms, artificial intelligence. 1 Introduction Since the pioneer work of Rosenberg in the late 60s regarding the possibility of using genetic-based search to deal with multiple objectives, this new area of research (now called evolutionary multiobjective optimization) has grown c...

Goal programming is a technique often used in engineering design activities primarily to find a compromised solution which will simultaneously satisfy a number of design goals. In solving goal programming problems, classical methods reduce the multiple goal-attainment problem into a single objective of minimizing a weighted sum of deviations from goals. Moreover, in tackling non-linear goal programming problems, classical methods use successive linearization techniques, which are sensitive to the chosen starting solution. In this paper, we pose the goal programming problem as a multi-objective optimization problem of minimizing deviations from individual goals. This procedure eliminates the need of having extra constraints needed with classical formulations and also eliminates the need of any user-defined weight factor for each goal. The proposed technique can also solve goal programming problems having nonconvex trade-off region, which are difficult to solve using classical methods. T...

Abstract—In this paper, we develop a performance model of a cell in a wireless communication network where the effect of handoff arrival and the use of guard channels is inlcuded. Fast recursive formulas for the loss probabilities of new calls and handoff calls are developed. Monotonicity properties of the loss probabilities are proven. Algorithms to determine the optimal number of guard channels and the optimal number of channels are given. Finally, a fixed-point iteration scheme is developed in order to determine the handoff arrival rate into a cell. The uniqueness of the fixed point is shown. Index Terms—Channel allocation, Markov models, optimization, performance modeling, wireless cellular networks.

In this paper we present a motivational mechanism to generate and determine the worth of goals and to represent various constraints involved in satisfying a goal. The work builds on the SMART agent framework and adds to the growing body of work that is attempting to extend the abilities of autonomous agents past the constraints of the traditional symbolic approaches to AI. The paper represents a first step in increasing an agent's autonomy in the domain of ecommerce, specifically enabling the agent to dynamically set issue parameters in relation to the importance of the issue and the effects of any existing constraints.

A scheduling problem with piecewise linear (PL) optimization extends conventional scheduling by imposing a conjunction of combinatorial PL constraints involving the objective function variables. The problem is decomposed into two constraint sub-problems 1) Resource feasibility constraints and 2) Temporal and PL constraints. This decomposition is fully exploited to apply a probe backtrack algorithm which hybridizes constraint reasoning and Linear Programming (LP) based probing. The paper investigates and compares dierent probing techniques and use the PL constraints can be used to tighten dynamically the sub-problem delegated to the prober.

Gabor-filter based methods have been successfully applied for a variety of machine vision applications, such as texture segmentation [1-10], edge detection [11], object detection [12,13], image representation [14], and recognition of handwritten numerals [15]. In this paper we consider the problem of segmenting textured images

The major objective of this paper is to examine the optimal routing in layout and location problems from a network optimization perspective where manufacturing facilities are modelled as open finite queueing networks with a multiobjective set of performance measures. The overall material handling system is broken down into a set of layout topologies. For each one of these topologies the optimal routing is determined so that the product throughput is maximized while minimizing the average sojourn time and holding costs. An approximate analytical decomposition technique for modelling open finite queueing networks, called the Generalized Expansion Method (GEM), developed by the authors, is utilized to calculate the desired outputs. A mathematical optimization procedure which is described in this paper is then used to determine the optimal routes. As will be demonstrated, the design methodology of combining the optimization and analytical queueing network models provides a very effective procedure for evaluating alternative topologies while simultaneously determining the average sojourn times and the maximum throughputs of the best routes.

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Abstract- In this paper, we investigate the availability requirement for the fault management server in high-availability communication systems. According to our study, we find that the availability of the fault management server does not need to be 99.999 % in order to guarantee a 99.999 % system availability as long as the fail-safe ratio (the probability that the failure of the fault management server will not bring the system down) and the fault coverage ratio (the probability that the failure in the system can be detected and recovered by the fault management server) are sufficiently high. Tradeoffs can be made among the availability of the fault management server, the fail-safe ratio and the fault coverage ratio to optimize system availability. A cost-effective design for the fault management server is proposed in this paper. Fault management plays an indispensable role in today’s high-availability communication system. Fault management involves techniques for rapidly detecting, isolating and recovering system from faults, either

this paper, we compare the performance of three typical and widely used optimization techniques for a specific MEG source localization problem. We first introduce a hybrid algorithm by combining genetic and local search strategies to overcome disadvantages of conventional genetic algorithms. Second, we apply the tabu search, a widely used optimization method in combinational optimization and discrete mathematics, to source localization. To the best of our knowledge, this is the first attempt in the literature to apply tabu search to MEG=EEG source localization. Third, in order to further compare the performance of the above algorithms, simulated annealing is also applied to MEG source localization problem. The computer simulation results show that our local genetic algorithm is the most effective approach to dipole localization, and the tabu search method is also a very good strategy for this problem. Keywords: Magnetoencephalogram (MEG); Dipoles; Global optimization; Genetic algorithms; Simulated annealing; Tabu search C. R. Categories: G.1.6, I.2.8 1