An Opposition-Based Beluga Whale Optimization

The population-based evolutionary algorithm simulates the behavior of different organisms. Randomness, diversity, exploration, and exploitation make the population quickly converge to the optimal solution in the evolution process. For the swarm intelligence algorithm, it is essential to balance the exploration and exploitation ability of the algorithm. This paper proposes an opposition-based beluga whale optimization (OBWO) and uses an adaptive strategy to improve the algorithm’s performance. To prove the capability of the OBWO, we verified the new algorithm on CEC_2013 and compared it with other excellent algorithms. The results show that the performance of the OBWO is first-rate and far exceeds that of the original algorithm.