Abstract
Particle Swarm Optimization (PSO) is a well-known technique that has been used for a wide range of optimization problems. The method is inherently parallel, wherein a group of particles wander in the solution space; communicate with one another to find the best solution. Though parallel, this method has not been much experimented in peer-to-peer computing frameworks. A peer-to-peer network brings a new set of challenges but has a number of distinct properties; for example they are prone to various types of failure but can harness the unused computing cycle of a set of systems. In this paper, we illustrate such a framework, wherein the PSO method is being implemented on top of a custom peer-to-peer network. Our framework includes novel algorithms that effectively skip overwork, finds Pareto optimal solutions that are diversified and includes both load balance and fault tolerance techniques. We demonstrate the use of this new distributed optimization framework using some well-known multi-objective benchmark functions and explain its effectiveness when compared to other systems of such types.
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Dewan, H., Nayak, R.B., Susheela Devi, V. (2013). A Peer-to-Peer Particle Swarm Optimizer for Multi-objective Functions. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2013. Lecture Notes in Computer Science, vol 8297. Springer, Cham. https://doi.org/10.1007/978-3-319-03753-0_64
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DOI: https://doi.org/10.1007/978-3-319-03753-0_64
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