A Comparison Study between Two Algorithms Particle Swarm Optimization for Depth Control of Underwater Remotely Operated Vehicle

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This paper investigates two algorithms based on particle swarm optimization (PSO) to obtain optimum parameter. In this research, an improved PSO algorithm using a priority-based fitness PSO (PFPSO) and priority-based fitness binary PSO (PFBPSO) approach. This comparison study between two algorithms applied on underwater Remotely Operated Vehicle for depth control.  Two parameters in Single Input Fuzzy Logic Controller will tune using two algorithms to obtain optimum parameter. There are two parameters to be tuned namely the break point and slope for the piecewise linear or slope for the linear approximation. The study also covered a comparison for time execution for every time the parameter tuning was done. Based on the results the PFBPSO gives a consistent value of optimum parameter and time execution very fast. The best optimum parameter of SIFLC determined using 2 methods such that average of optimum parameter and intersection of y-axis. The PFBPSO gives comparative results in term of two parameters and time execution very fast compared with improved PSO.
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Priority Fitness PSO; Priority Fitness Binary PSO; Optimum Parameter; Single Input FLC; Time Execution

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