Stochastic Marine Predator Algorithm with Multiple Candidates

work proposes a metaheuristic algorithm that modifies the marine predator algorithm (MPA), namely, the stochastic marine predator algorithm with multiple candidates (SMPA-MC). The modification is conducted in several aspects. The proposed algorithm replaces the three fixed equal size iteration phases with linear probability. Unlike the original MPA, in this proposed algorithm, the selection between exploration and exploitation is conducted stochastically during iteration. In the beginning, the exploration-dominant strategy is implemented to increase the exploration probability. Then, during the iteration, the exploration probability decreases linearly. Meanwhile, the exploitation probability increases linearly. The second modification is in the prey's guided movement. Different from the basic MPA, where the prey moves toward the elite with small step size, several candidates are generated with equal inter candidate distance in this work. Then, the best candidate is chosen to replace the prey's current location. The proposed algorithm is then implemented to solve theoretical mathematic functions and a real-world optimization problem in production planning. The simulation result shows that in the average fitness score parameter, the proposed algorithm is better than MPA, especially in solving multimodal functions. The simulation result also shows that the proposed algorithm creates 9%, 19%, and 30% better total gross profit than particle swarm optimization, marine predator algorithm, and Komodo mlipir algorithm, respectively.