Evolutionary Particle Swarm Optimisation for Two Dimensional Bin Packing Problem
Swarm intelligence meta-heuristics are widely used in solving continuous optimisation problems. However application of swarm intelligence meta-heuristics to combinatorial optimisation problems is limited, especially to cutting and packing problem which is a core area of research for many decades. EP...
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| Format: | Thesis |
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2013
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| Summary: | Swarm intelligence meta-heuristics are widely used in solving continuous optimisation problems. However application of swarm intelligence meta-heuristics to combinatorial optimisation problems is limited, especially to cutting and packing problem which is a core area of research for many decades. EPSO – Evolutionary Particle Swarm Optimisation is the hybrid version of the mainstream swarm intelligence meta-heuristic known as Particle Swarm Optimisation (PSO). The bin packing problem (BPP) is a classical combinatorial optimisation problem which has wide real-life applications: loading of boxes to pallets, trucks and containers, packing of box bases on shelves and other applications in the wood and metal industry. The non-oriented two-dimensional bin packing problem (NO-2DBPP) is a non-trivial variant of BPP where the objective is to allocate without overlapping but allowing the pieces to be rotated by 90 degree to a minimum number of bins. The focus of this thesis is to apply and investigate the efficiency of EPSO methodology for solving the NO2DBPP. |
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