Sanaz Mostaghim

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In this paper, the influence of -dominance on Multi-objective Particle Swarm Optimization (MOPSO) methods is studied. The most important role of dominance is to bound the number of non-dominated solutions stored in the archive (archive size), which has influences on computational time, convergence and diversity of solutions. Here, -dominance is compared(More)
Covering the whole set of Pareto-optimal solutions is a desired task of multi-objective optimization methods. Because in general it is not possible to determine this set, a restricted amount of solutions are typically delivered in the output to decision makers. In this paper, we propose a new method using multi-objective particle swarm optimization to cover(More)
In recent years, a number of authors have successfully extended particle swarmoptimization to problem domains with multiple objec\-tives. This paper addresses theissue of parallelizing multi-objec\-tive particle swarms. We propose and empirically comparetwo parallel versions which differ in the way they divide the swarminto subswarms that can be processed(More)
Many practical optimization problems are constrained, and have a bounded search space. In this paper, we propose and compare a wide variety of bound handling techniques for particle swarm optimization. By examining their performance on flat landscapes, we show that many bound handling techniques introduce a significant search bias. Furthermore, we compare(More)
In this chapter, we propose an approach for the synthesis of heterogenous embedded systems, including allocation and binding problems. For solving these in general NP-complete problems, Evolutionary Algorithms have been proven to provide good solutions for search spaces of moderate size. For realistic embedded system applications, however, two more(More)
All existing stochastic optimisers such as Evolutionary Algorithms require parameterisation which has a significant influence on the algorithm’s performance. In most cases, practitioners assign static values to variables after an initial tuning phase. This parameter tuning method requires experience the practitioner may not have and, when done(More)
In MOEAs with elitism, the data structures and algorithms for storing and updating archives may have a great impact on the CPU time, especially when optimizing continuous problems with larger population sizes. In this paper, we introduce Quad-trees as an efficient data structure for storing Pareto-points. Apart from conventional linear lists, we have(More)