Enhanced particle swarm optimization algorithms with robust learning strategy for global optimization
| dc.contributor.author | Lim, Wei Hong | |
| dc.date.accessioned | 2016-11-01T08:25:13Z | |
| dc.date.available | 2016-11-01T08:25:13Z | |
| dc.date.issued | 2014-09 | |
| dc.description.abstract | Particle Swarm Optimization (PSO) is a metaheuristic search (MS) algorithm inspired by the social interactions of bird flocking or fish schooling in searching for food sources. Although the original PSO is an effective optimization technique to solve the global optimization problem, this algorithm suffers with several drawbacks in solving the high dimensional and complex problems, such as slow convergence rate, high tendency to be trapped into the local optima, and difficulty in balancing the exploration/exploitation. To mitigate these drawbacks, this research has proposed four enhanced PSO variants, namely, Teaching and Peer-Learning PSO (TPLPSO), Adaptive Two-Layer PSO with Elitist Learning Strategy (ATLPSO-ELS), PSO with Adaptive Time-Varying Topology Connectivity (PSO-ATVTC), and PSO with Dual-Level Task Allocation (PSO-DLTA). An alternative learning phase is proposed into the TPLPSO to offer the new search direction to the particles which fail to improve its fitness during the previous learning phase. Two adaptive mechanisms of task allocation are proposed into the ATLPSO-ELS to enhance the algorithm’s capability in balancing the exploration/exploitation during the optimization process. Being a PSO variant equipped with multiple learning strategies, PSO-ATVTC has an effective and efficient mechanism to adaptively adjust the exploration and exploitation strengths of different particles, by systematically manipulating their respective neighborhood structures. Unlike most existing PSO variants, PSO-DLTA has the capability of performing the dimension-level task allocation. Specifically, the dimension-level task allocation (DTA) module proposed into the PSO-DLTA is able to assign different search tasks to different dimensional components of a particle, based on the unique distance characteristics between the particle and the global best particle in each dimension. The overall performances of the four proposed PSO variants have been compared with a number of existing PSO variants and other MS algorithms on 30 benchmark functions with different characteristics and three real-world engineering design problems. The experimental results obtained by each proposed PSO variant are also thoroughly evaluated and verified via the non-parametric statistical analyses. Based on the experiment results, TPLPSO is observed to have the lowest computational complexity and this algorithm exhibits excellent search accuracy, search reliability, and search efficiency in solving simpler benchmark functions. ATPLSO-ELS achieves significant performance improvement, in terms of search accuracy, search reliability, and search efficiency, in solving more challenging benchmark functions, with the cost of increasing computational complexity. Meanwhile, PSO-ATVTC and PSO-DLTA successfully solve the benchmark functions with different characteristics with promising search accuracy, search reliability, and search efficiency, without severely compromising the complexities of algorithmic frameworks. Among the four proposed PSO variants, PSO-ATVTC is concluded as the best performing variant, considering that this algorithm yields the most significant performance improvement, by incurring the second lowest computational complexity | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2948 | |
| dc.subject | The original PSO is an effective optimization technique | en_US |
| dc.subject | to solve the global optimization problem, | en_US |
| dc.title | Enhanced particle swarm optimization algorithms with robust learning strategy for global optimization | en_US |
| dc.type | Thesis | en_US |
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