Improved Sparrow Search Algorithm Combining Ranking-Based Elastic Collision

Abstract

To improve the shortcomings of the sparrow search algorithm (SSA), such as the loss of diversity due to inadequate population initialization results and the susceptibility to interference from individual location information during exploration and exploitation, which affect the accuracy of the search, an improved sparrow search algorithm based on fusion ranking elastic collision, referred to as XSSA, is proposed. Firstly, an improved iterative chaotic map with infinite collapses (ICMIC) improves the dispersion degree of the initial population distribution. Then, the Gaussian random walk strategy is used to balance the exploration and development capabilities of the algorithm. In addition,  sorted elastic collision policy is imposed on all individuals after the discoverer is updated, which avoids premature convergence of the algorithm to the local extreme value. Finally, a multi-strategy boundary processing mechanism is formulated according to the characteristics of optimization at different stages to retain the population and avoid the loss of diversity. At the same time, the position of individuals beyond the boundary is re-updated in combination with important position information, so that the processed position is more reasonable and provides quality guarantee for the next iterative search. The simulation experiments are performed on 12 benchmark functions, and the convergence accuracy graph demonstrates the performance of the algorithm. By means of contribution tests for each strategy, Wilcoxon rank sum test and the comprehensive ranking of Friedman test, the effectiveness, uniqueness and better optimization performance of XSSA are proven.

Keywords

• sparrow search algorithm (ssa); iterative chaotic map with infinite collapses (icmic); gauss walk learning (gwl); ranking-based elastic collision; multi-strategy boundary processing mechanism