Subtraction-Average-Based Optimizer: A New Swarm-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Evolutionary Computation

Background:

• Optimization problems are prevalent across scientific and engineering disciplines.
• Existing metaheuristic algorithms face challenges in balancing exploration and exploitation.
• Novel approaches are needed to enhance the efficiency and effectiveness of optimization techniques.

Purpose of the Study:

• Introduce a new evolutionary-based optimization algorithm, the Subtraction-Average-Based Optimizer (SABO).
• To mathematically model and implement the SABO for diverse optimization tasks.
• To evaluate the performance of SABO against established algorithms on benchmark and real-world problems.

Main Methods:

• Developed the Subtraction-Average-Based Optimizer (SABO) inspired by the subtraction average of searcher agents.
• Tested SABO on 52 standard benchmark functions (unimodal, multimodal, CEC 2017).
• Applied SABO to four engineering design problems to assess real-world applicability.

Main Results:

• SABO effectively balances exploration and exploitation for robust optimization.
• Achieved superior and competitive results compared to twelve well-known metaheuristic algorithms on benchmark functions.
• Demonstrated enhanced performance in solving real-world engineering design problems, yielding more optimal solutions.

Conclusions:

• The proposed Subtraction-Average-Based Optimizer (SABO) is a highly effective and competitive algorithm for various optimization challenges.
• SABO shows significant potential for practical applications in engineering and other fields requiring advanced optimization.
• The subtraction-average mechanism provides a robust strategy for enhancing search efficiency and solution quality.