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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 significant search bias. Furthermore, we compare the performance of many bound handling techniques on a variety of test problems, demonstrating that the bound handling technique can have a major impact on the algorithm performance, and that the method recently proposed as standard does generally not perform well.

In this paper, the influence of e-dominance on Multi-objective Particle Swarm Optimization (MOPSO) methods is studied. The most important role of edominance 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, e-dominance is compared with the existing clustering technique for k i n g the archive size and the solutions are compared in terms of computational time, convergence and diversity. A new diversity metric is also suggested. The results show that the e-dominance method can find solutions much faster than the clustering technique with comparable and even in some cases better convergence and diversity.

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