Basketball team optimization algorithm (BTOA): a novel sport-inspired meta-heuristic optimizer for engineering...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Real-world optimization problems are increasingly high-dimensional, nonlinear, and constrained, challenging existing metaheuristics.
• Mainstream population-based methods often suffer from premature convergence and poor exploration-exploitation balance in complex scenarios.
• The No Free Lunch theorem necessitates domain-specific optimizers for diverse problem classes.

Purpose of the Study:

• To introduce a novel sports-inspired metaheuristic, the Basketball Team Optimisation Algorithm (BTOA), to address limitations in existing optimization techniques.
• To enhance global exploration and manage individual diversity through extensible modules for broader applicability.
• To validate BTOA's efficacy on benchmark functions and complex real-world engineering optimization tasks.

Main Methods:

• Developed the Basketball Team Optimisation Algorithm (BTOA) by mapping basketball concepts (training, fast breaks, positioning, passing) to cooperative search operators.
• Introduced two extensible modules: a dynamic positioning strategy for enhanced global exploration and a VariableAttributes module for diversity management.
• Conducted extensive experiments on CEC2005 and CEC2017 benchmark suites across multiple dimensions (30, 50, 100) and evaluated performance on UAV path planning.

Main Results:

• BTOA achieved the lowest mean error on 82.61% of CEC2005 functions and a significant majority of CEC2017 functions across tested dimensions.
• Statistical tests (Wilcoxon signed-rank, Friedman) confirmed the significance of BTOA's performance gains over other algorithms.
• BTOA demonstrated strong performance on real-world problems with complex constraints and large decision spaces, such as UAV path planning.

Conclusions:

• The proposed Basketball Team Optimisation Algorithm (BTOA) offers a scalable and reliable solution for contemporary engineering optimization tasks.
• BTOA effectively alleviates key shortcomings of existing metaheuristics, particularly in handling high-dimensional, nonlinear, and constrained problems.
• The extensible modules enhance the heuristic design space and can be integrated into other population-based optimizers.