Multi-target particle swarm optimization algorithm based on ASU strategy
A multi-objective particle swarm, optimization algorithm technology, applied in computing, genetic models, instruments, etc., can solve the problem of inapplicable high-dimensional models, lack of regional particles, and can not well maintain the diversity of solutions and the uniformity of distribution, etc. problems to achieve the effect of maintaining uniformity and diversity, improving reliability, and improving uniformity and diversity
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[0026] The present invention will be described in detail below in conjunction with accompanying drawing and embodiment: figure 1 As shown, the multi-objective particle swarm optimization algorithm based on the ASU strategy of the present invention comprises the following steps:
[0027] (1) Population initialization. Set the elite set to be empty; the number of iterations t=0, within the range of control variables, randomly initialize the particle swarm, the individual extremum and the global extremum of each particle are the initial positions, and set the scale of the elite set;
[0028] (2) Calculate the fitness value of the objective function of each particle;
[0029] (3) Judging whether the end criterion is met, the maximum allowable number of iterations is reached or the value of the objective function corresponding to the optimal solution changes less than a given value within a given number of iteration steps, stop optimization and output the result to form a Pareto o...
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