The Fruit Fly Optimization algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization algorithm ...
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The Fruit Fly Optimization algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, Fruit Fly Optimization algorithm tends to converge prematurely. In response to this situation, this article proposes a new optimized structure-quasi-affinetransformationevolutionary for the Fruit fly Optimization algorithm. The new algorithm uses the evolution matrix in quasi-affine transformation evolution algorithm to update the position coordinates of particles. This strategy makes the movement of particles more scientific and the search space broader. In order to prove its effectiveness, we compare quasi-affinetransformationevolutionary for the Fruit fly Optimization algorithm with five other mature intelligent algorithms, and test them on 22 different types of benchmark functions. In order to observe the multi-faceted performance of quasi-affinetransformationevolutionary for the Fruit fly Optimization algorithm more intuitively, we also conduct experiments on algorithm convergence analysis, the Friedman test, the Wilcoxon signed-rank test, and running time comparison. Through the above several comparative experiments, quasi-affinetransformationevolutionary for the Fruit fly Optimization algorithm has indeed demonstrated its strong competitiveness. Finally, we apply it to Capacitated Vehicle Routing Problem. Through comparing with the contrast algorithms, it is confirmed that quasi-affinetransformationevolutionary for the Fruit fly Optimization algorithm can achieve better vehicle routes planning.
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