Pair barracuda swarm optimization algorithm a natural-inspired metaheuristic method for high dimensional optimization problems

Abstract The high-dimensional optimization is a new research difficulty in the field of intelligent computing.Traditional evolutionary tools are prone to dimensional catastrophes and local optimal in high dimensional spaces, thus failing to provide highly accurate results. In this paper, to solve the high-dimensional optimization problem, a novel pair barracuda swarm optimization algorithm (PBSOA) is proposed. In PBSOA, a novel construction of barracuda pairs is proposed to reduce dimensional catastrophes.A support barracuda is also used to enhance the global search ability of the leader barracuda pair.To verify the performance of the barracuda algorithm, the CEC2017 standard function is used for experiments, and five excellent optimization algorithms are selected in control groups. Finally, in a total of 29 test functions, PBSOA gets 18 firsts, 4 seconds, 3 thirds, 3 fourths, and 1 sixth, and the average rank is 1.83.The experimental results demonstrate that PBSOA presents the best performance in all algorithms and is able to provide trustworthy results for high-dimensional optimization problems.