An Improved Mantis Search Algorithm for Solving Optimization Problems

The traditional mantis search algorithm (MSA) suffers from limitations such as slow convergence and a high likelihood of converging to local optima in complex optimization scenarios. This paper proposes an improved mantis search algorithm (IMSA) to overcome these issues. An adaptive probability conversion factor is designed, which adaptively controls the proportion of individuals entering the search phase and the attack phase so that the algorithm can smoothly transition from large-scale global exploration to local fine search. In the search phase, a probability update strategy based on both subspace and full space is designed, significantly improving the adaptability of the algorithm to complex problems by dynamically adjusting the search range. The elite population screening mechanism, based on Euclidean distance and fitness double criteria, is introduced to provide dual guidance for the evolution direction of the algorithm. In the attack stage, the base vector adaptive probability selection mechanism is designed, and the algorithm’s pertinence in different optimization stages is enhanced by dynamically adjusting the base vector selection strategy. Finally, in the stage of sexual cannibalism, the directed random disturbance update method of inferior individuals is adopted, and the population is directly introduced through the non-greedy replacement strategy, which effectively overcomes the loss of population diversity. The experimental results of 29 test functions on the CEC2017 test set demonstrate that the IMSA exhibits significant advantages in convergence speed, calculation accuracy, and stability compared to the original MSA and the five best meta-heuristic algorithms.