One-to-One Based Optimizer (OOBO)

OOBO: A New Metaheuristic Algorithm for Solving Optimiza-tion Problems

Abstract: This study proposes the One-to-One Based Optimizer (OOBO), a new optimization technique for solving optimization problems in various scientific areas. The key idea in designing the suggested OOBO is to effectively use the knowledge of all members in the process of updating the algorithm population while avoiding the algorithm from relying on specific members of the population. We use a one-to-one correspondence between the two sets of population members and the members selected as guides to increase the involvement of all population members in the update process. Each population member is chosen just once as a guide and is only utilized to update another member of the population in this one-to-one interaction. The proposed OOBO’s performance in optimization is evaluated on fifty-two objective functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results highlight the remarkable capacity of OOBO to strike a balance between exploration and exploitation within the problem-solving space during the search process. The quality of the optimization results achieved by the proposed OOBO is evaluated by comparing them to eight well-known algorithms. The simulation findings show that the OOBO outperforms the other algorithms in addressing optimization problems and can give more acceptable quasi-optimal solutions. Also, the implementation of OOBO on six engineering problems shows the effectiveness of the proposed approach in solving the real-world optimization applications.