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DS-PSO: Particle Swarm Optimization with Dynamic and Static Topologies

Date: 2017-05-01

Creator: Dominick Sanchez

Access: Open access

Particle Swarm Optimization (PSO) is often used for optimization problems due to its speed and relative simplicity. Unfortunately, like many optimization algorithms, PSO may potentially converge too early on local optima. Using multiple neighborhoods alleviates this problem to a certain extent, although premature convergence is still a concern. Using dynamic topologies, as opposed to static neighborhoods, can encourage exploration of the search space at the cost of exploitation. We propose a new version of PSO, Dynamic-Static PSO (DS-PSO) that assigns multiple neighborhoods to each particle. By using both dynamic and static topologies, DS-PSO encourages exploration, while also exploiting existing knowledge about the search space. While DS-PSO does not outperform other PSO variants on all benchmark functions we tested, its performance on several functions is substantially better than other variants.

GEM-PSO: Particle Swarm Optimization Guided by Enhanced Memory

Date: 2019-05-01

Creator: Kevin Fakai Chen

Access: Open access

Particle Swarm Optimization (PSO) is a widely-used nature-inspired optimization technique in which a swarm of virtual particles work together with limited communication to find a global minimum or optimum. PSO has has been successfully applied to a wide variety of practical problems, such as optimization in engineering fields, hybridization with other nature-inspired algorithms, or even general optimization problems. However, PSO suffers from a phenomenon known as premature convergence, in which the algorithm's particles all converge on a local optimum instead of the global optimum, and cannot improve their solution any further. We seek to improve upon the standard Particle Swarm PSO algorithm by fixing this premature convergence behavior. We do so by storing and exploiting increased information in the form of past bests, which we deem enhanced memory. We introduce three types of modifications to each new algorithm (which we call a GEM-PSO: Particle Swarm Optimization, Guided by Enhanced Memory, because our modifications all deal with enhancing the memory of each particle). These are procedures for saving a found best, for removing a best from memory when a new one is to be added, and for selecting one (or more) bests to be used from those saved in memory. By using different combinations of these modifications, we can create many different variants of GEM-PSO that have a wide variety of behaviors and qualities. We analyze the performance of GEM-PSO, discuss the impact of PSO's parameters on the algorithms' performances, isolate different modifications in order to closely study their impact on the performance of any given GEM-PSO variant, and finally look at how multiple modifications perform. Finally, we draw conclusions about the efficacy and potential of GEM-PSO variants, and provide ideas for further exploration in this area of study. Many GEM-PSO variants are able to consistently outperform standard PSO on specific functions, and GEM-PSO variants can be shown to be promising, with both general and specific use cases.