A new human-based offensive defensive optimization algorithm for solving optimization problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Single-objective optimization problems are prevalent in science and engineering.
• Existing metaheuristic algorithms often struggle with balancing exploration and exploitation, leading to local minima.
• Human-inspired strategies offer novel approaches to enhance optimization performance.

Purpose of the Study:

• Introduce a novel human-inspired metaheuristic algorithm, Offensive Defensive Optimization (ODO).
• Evaluate ODO's performance against established algorithms on benchmark and real-world problems.
• Assess ODO's efficiency in exploration, exploitation, and convergence.

Main Methods:

• Developed the Offensive Defensive Optimization (ODO) algorithm based on board game strategies.
• Tested ODO on Congress on Evolutionary Computation (CEC) 2017 and 2022 benchmark suites.
• Applied ODO to two real-world engineering design optimization problems.

Main Results:

• ODO demonstrated superior performance in 80% of CEC2017 and 72% of CEC2022 cases compared to eight other algorithms.
• Statistically significant improvements were observed in ODO's performance.
• ODO showed effective convergence, robust exploration/exploitation balance, and superiority in engineering problems.

Conclusions:

• The Offensive Defensive Optimization algorithm is a highly effective approach for single-objective optimization.
• ODO provides a superior and competitive alternative to existing metaheuristic methods.
• The algorithm's human-inspired hybrid search framework enhances its ability to overcome local optima.