Multiple Interaction Optimizer: A Novel Metaheuristic and Its Application to Solve Order Allocation Problem

Abstract

Order allocation problem is a crucial problem in the manufacturing system. Many studies have been carried out to overcome this problem. On the other hand, future studies related to this problem are still available due to its complexity, circumstance, and methods. This work develops a new metaheuristic called a multiple interaction optimizer (MIO). MIO has distinct mechanics in finding the optimal solution. MIO consists of two phases. In the first phase, each agent interacts with some randomly selected agents in the population. The guided search is conducted in every interaction. In the second phase, each agent carries out a local search which linearly reduces the search space during the iteration. Three tests are carried out on the performance of MIO. In the first test, the MIO is challenged to solve 23 functions. The second test is performed as the investigation of the hyper parameters. In the third test, MIO is challenged to solve the order allocation problem with the objective is minimizing the total cost, total lateness, and total defect. In this test, MIO is benchmarked with five metaheuristics: particle swarm optimization (PSO), marine predator algorithm (MPA), grey wolf optimizer (GWO), slime mould algorithm (SMA), and golden search optimizer (GSO). The result indicates that MIO is superior in solving both 23 functions and order allocation problems. In the theoretical test, MIO outperforms PSO, MPA, GWO, SMA, and GSO in 22, 21, 22, 19, and 18 functions respectively. MIO is also superior in achieving minimum cost, minimum lateness, and minimum total defect in solving order allocation problems