Application of a novel metaheuristic algorithm inspired by stadium spectators in global optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Metaheuristic algorithms often require extensive parameter tuning for optimal performance.
• There is a need for parameter-free optimization methods that can efficiently solve diverse problems.
• Novel algorithms are crucial for advancing computational intelligence and problem-solving capabilities.

Purpose of the Study:

• To introduce a novel parameter-free metaheuristic algorithm, the Stadium Spectators Optimizer (SSO).
• To mathematically model and evaluate the performance and efficiency of the SSO algorithm.
• To compare the SSO algorithm against established optimization techniques on benchmark functions.

Main Methods:

• Development of the Stadium Spectators Optimizer (SSO) algorithm, inspired by crowd behavior.
• Mathematical modeling and implementation of the SSO algorithm.
• Performance evaluation using standard mathematical test functions and CEC-BC-2017 benchmark suites across various dimensions.

Main Results:

• The SSO algorithm demonstrates parameter-free optimization, eliminating the need for additional parameter setup.
• SSO shows comparable and robust performance with state-of-the-art techniques on 14 mathematical test functions.
• While EBOwithCMAR outperformed SSO on some CEC-BC-2017 functions, SSO ranked second and outperformed CMA-ES, indicating competitive performance.

Conclusions:

• The SSO algorithm presents a viable parameter-free approach to optimization, simplifying application.
• SSO exhibits competitive performance against established algorithms, particularly in its ability to solve problems without parameter tuning.
• Further research may explore enhancing SSO's performance in higher dimensions and on more complex, real-world optimization tasks.