Hybrid Marine Predator Algorithm and Hide Object Game Optimization.

This work presents a hybrid metaheuristic, namely a hybrid marine predator algorithm - hide object game optimization (MPA-HOGO). MPA-HOGO is built by combining two shortcoming metaheuristics: marine predator algorithm (MPA) and hide object game optimization (HOGO). The distinct strategy in MPA-HOGO is as follows. MPA-HOGO utilizes an exploration-to-exploitation strategy gradually as the iteration goes on. It uses the global best solution evaluated in every iteration to ensure that the final solution comes from the best solution found during the iteration. It also eliminates the normalized score of every solution to reduce the computational process. This metaheuristic is then tested by solving 23 classic functions. MPA-HOGO is benchmarked with particle swarm optimization (PSO), Komodo mlipir algorithm (KMA), MPA, and HOGO. The result indicates that MPA-HOGO is superior among these metaheuristics. MPA-HOGO is also competitive in solving practical optimization problems. Through simulation, MPA-HOGO produces 13%, 7%, and 2% lower total costs than PSO, MPA, and HOGO respectively in solving inventory management problems in the vendor-managed inventory system. [ABSTRACT FROM AUTHOR]