Include-Exclude Optimization: A New Metaheuristic and Its Application to Handle Optimization Problems in Electrical and Mechanical Engineering

There are two challenges in the development of new metaheuristics. The first challenge is the utilization of the improving status of agents. The second challenge is the employment of stagnation avoidance strategies. This work introduces a new metaheuristic called include-exclude optimization (IEO). It provides a novel approach by combining both the quality and improving statuses of agents to construct the reference and determine the direction of the guided search or movement. IEO also proposes a new technique to avoid stagnation by accepting the best solution candidate when stagnation occurs after the agent performs three guided searches. Then, IEO is challenged to solve three use cases. The first use case is the 23 traditional functions. The second use case is ELD problem representing practical problem in electrical engineering field. The third use case is gear train design problem representing practical problem in mechanical engineering field. In this assessment, IEO is benchmarked with five new metaheuristics: golden search optimization (GSO), total interaction algorithm (TIA), dollmaker optimization algorithm (DOA), carpet weaver optimization (CWO), and hiking optimization (HO). The result shows that IEO is superior to these five metaheuristics in handling 23 traditional functions and performs the best in handling ELD problem and gear train design problem.