Study on the Influence of Feasible Domain Connectivity on the Optimization Performance of Intelligent Algorithms
At present,there are few studies on the influence of changes in search space characteristics such as feasible domain connectivity on the optimization efficiency and global optimization ability of algorithms.Based on the above background,the differences between the three typical intelligent algorithms of GA(Genetic Algo-rithm),DE(Differential Evolution)and PSO(Particle Swarm Optimization)are compared and analyzed,and the basic mechanism of DE algorithms with stronger global optimization ability is pointed out.Then,three optimi-zation models of connected feasible domain,incommensurable subdomain size consistency and incommensible feasible subdomain size inconsistency designed by using four sets of test functions are solved using three algo-rithms,and the results are compared and analyzed.The analysis shows that the optimization performance of the DE algorithm is far better than that of the GA and PSO algorithms,which is basically consistent with the conclu-sions of previous research.At the same time,it is found that for the high-dimensional optimization problem of the global optimal solution in the smaller feasible domain when the feasible domain is not connected,the DE algo-rithm is also difficult to find the global optimal solution,and the existing intelligent algorithm still cannot fully meet the actual needs,so a new adaptive differential evolution algorithm based on global optimization is pro-posed.
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