Convex combination multiple populations competitive swarm optimization for moving target search using UAVs.

Unmanned aerial vehicles (UAVs) searching for a moving target is a complex search optimization problem that aims to find a path with maximum probability of observing the target. However, as the area where the target may be located increases in size, existing methods tend to cause easy entrapment into local optima, resulting in poor performance. Hence, we propose a Convex Combination Multiple Populations Competitive Swarm Optimization algorithm (CDCSO) in this paper. First, we suggest applying the multiple population strategy to enhance the search ability of the algorithm. The population of interest is divided into two subgroups that each conduct the CSO. Second, we propose a novel convex combination update strategy to coordinate the two subgroups to search jointly for the global optimal solution. At each iteration, this strategy allows the two subgroups to compete and learn from each other, which prevents the population from falling into the local optimum. We then designed new scenarios containing 3 to 6 probability areas to investigate the performance of our proposed algorithm in multiple and complex scenarios.