Abstract
Multi-objective optimization problem is an important part in solving a wide number of engineering and scientific applications. To-date, most of the research has been conducted in solving static multi-objective problems where the decision variables and/or the objective functions do not change over a period of time. In a dynamic environment, the particles non dominated solution set during a specific iteration may no longer be valid due to change in the underlying system. As a result, traditional techniques for solving static multi-objective functions cannot be applied for solving dynamic multi-objective functions. Further, with the increase in the number of variables/objective functions, a single system based optimizer will take a long time to compute the non-dominated solution set. In this paper, we present a peer-to-peer distributed particle swarm optimization algorithm that tracks the change in the underlying system and is able to produce a diversified and dense non- dominated set using a network of peer-to-peer system. Our algorithms are tested using a set of known benchmark problems and results are reported. To our knowledge, this algorithm is the first of its kind in the areas of peer-to-peer particle swarm optimization.
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Dewan, H., Nayak, R.B., Devi, V.S. (2013). A Peer-to-Peer Dynamic Multi-objective Particle Swarm Optimizer. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_46
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DOI: https://doi.org/10.1007/978-3-319-03844-5_46
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