OOBO: A New Metaheuristic Algorithm for Solving Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Applied Mathematics

Background:

• Optimization problems are prevalent across scientific disciplines.
• Existing algorithms may exhibit limitations in population member engagement and exploration-exploitation balance.
• Novel optimization techniques are needed to address complex, real-world challenges.

Purpose of the Study:

• To introduce the One-to-One-Based Optimizer (OOBO), a novel metaheuristic optimization algorithm.
• To enhance the involvement of all population members in the optimization process.
• To improve the balance between exploration and exploitation in optimization algorithms.

Main Methods:

• Developed the One-to-One-Based Optimizer (OOBO) utilizing a one-to-one correspondence for population updates.
• Evaluated OOBO performance on 52 objective functions from the CEC 2017 test suite (unimodal, multimodal, high-dimensional).
• Compared OOBO against eight established optimization algorithms.

Main Results:

• OOBO demonstrated a superior ability to balance exploration and exploitation.
• The proposed optimizer achieved more acceptable quasi-optimal solutions compared to existing algorithms.
• OOBO outperformed other algorithms in addressing diverse optimization problems.

Conclusions:

• The One-to-One-Based Optimizer (OOBO) is an effective new technique for various optimization tasks.
• OOBO shows significant promise for solving complex scientific and engineering optimization problems.
• The algorithm's unique update mechanism contributes to its enhanced performance and applicability.