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This paper introduces a modified PSO, gradient particle swarm optimizer (GPSO), for geometric constraint solving. GPSO combines the merits of global search of the PSO and the sheer convergence capacity of gradient algorithm, the most prominent iterative method for linear equations. GPSO uses PSO to search the area where the best solution may exist in the whole space, and then performs fine searching...
To solve the constraint multi-solution problem, the constraints are separated to two sets, the original constraint set and the additional constraint set. First, the solver finds out multiple solutions. Then genetic algorithm and ant algorithm are combined in the process of searching optimal solution. We adopt genetic algorithm in the former process to produce the initiatory distribution of information...
When transferring the geometric constraint equation group into the optimization model, we need a method to jump out of the local beat solution so that we can find a global best solution. Considering the speed and global capability, we adopt compound particle group optimization algorithm. Particle swarm optimization algorithm is a kind of evolution computation technology based on group intelligence...
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